This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.
Osteoporosis is the fourth most common chronic disease worldwide. The adoption of preventative measures and effective self-management interventions can help improve bone health. Mobile health (mHealth) technologies can play a key role in the care and self-management of patients with osteoporosis.
This study presents a systematic review and meta-analysis of the currently available mHealth apps targeting osteoporosis self-management, aiming to determine the current status, gaps, and challenges that future research could address, as well as propose appropriate recommendations.
A systematic review of all English articles was conducted, in addition to a survey of all apps available in iOS and Android app stores as of May 2021. A comprehensive literature search (2010 to May 2021) of PubMed, Scopus, EBSCO, Web of Science, and IEEE Xplore was conducted. Articles were included if they described apps dedicated to or useful for osteoporosis (targeting self-management, nutrition, physical activity, and risk assessment) delivered on smartphone devices for adults aged ≥18 years. Of the 32 articles, a random effects meta-analysis was performed on 13 (41%) studies of randomized controlled trials, whereas the 19 (59%) remaining studies were only included in the narrative synthesis as they did not provide enough data.
In total, 3906 unique articles were identified. Of these 3906 articles, 32 (0.81%) articles met the inclusion criteria and were reviewed in depth. The 32 studies comprised 14,235 participants, of whom, on average, 69.5% (n=9893) were female, with a mean age of 49.8 (SD 17.8) years. The app search identified 23 relevant apps for osteoporosis self-management. The meta-analysis revealed that mHealth-supported interventions resulted in a significant reduction in pain (Hedges
Osteoporosis apps have the potential to support and improve the management of the disease and its symptoms; they also appear to be valuable tools for patients and health professionals. However, most of the apps that are currently available lack clinically validated evidence of their efficacy and focus on a limited number of symptoms. A more holistic and personalized approach within a cocreation design ecosystem is needed.
PROSPERO 2021 CRD42021269399; https://tinyurl.com/2sw454a9
Osteoporosis, or porous bone, is a serious chronic disease in which the density of bones is silently and progressively reduced, resulting in a more porous and fragile structure [
The current landscape of a rapidly aging population, accompanied by multiple chronic conditions, presents numerous challenges to optimally supporting the complex needs of this group. Therefore, it is essential to find better and affordable alternatives to hospital and institutional care that can support older adults in their homes rather than moving them to health care providers. The use of health-related mobile apps, or mobile health (mHealth), has emerged as an important and useful tool for improving health outcomes in chronic disease self-management [
The number of apps available on the planet exceeds 8 million, of which 60% are available on both Android and iOS app stores [
The motivation behind this systematic review stems from the fact that, to the best of our knowledge, there is no other review so far that explores mHealth apps dedicated to osteoporosis self-management available in both the web-based app market and in the research field. The present systematic review and meta-analysis were undertaken to come up with the identification of the current status of osteoporosis-related mHealth solutions, reveal any lack of functionalities, identify challenges and barriers, and propose recommendations for more personalized and effective remote health care monitoring and interventions. In this way, efforts toward the development and testing of a holistic mobile app to support patients at risk of or with osteoporosis are better informed. Osteoporosis self-management apps with a holistic approach should comprise a wide variety of features, including nutrition, physical exercise, medication, and performance monitoring, in addition to involving a wide spectrum of stakeholders, from rheumatologists to other health care professionals, and requiring patients to be well-informed and to take an active role in their own car, while providing an incentive for physicians to trust, integrate, and implement mHealth apps into their medical practice.
For this systematic review, published sources were identified by searching PubMed, Scopus, Web of Science, IEEE Xplore, and EBSCO databases. A comprehensive combination of keywords was used to have the maximum possible coverage:
The inclusion criteria were original studies or research papers, including people (both male and female) aged ≥18 years with no mental health conditions. The selected studies evaluated digital health technology, primarily designed to support targeted patient communication, education, diagnosis, real-time monitoring, and empowerment in the form of mobile phone apps supported by other audiovisual technologies. Moreover, we considered studies that use intelligent wireless sensors to capture any critical vital signs to support patients with osteoporosis in the long-term self-management of the disease. We also included studies that proposed a design or framework for mHealth apps targeting patients with osteoporosis. As no mHealth apps dedicated to osteoporosis self-management were found before 2010, only full-text studies published in peer-reviewed journals and in English from January 2010 to May 2021 were included.
Studies with participants who had mental disorders were excluded. In addition to studies that did not have full text available, we eliminated reviews, posters, letters, and expert opinion publications. We also did not consider studies with technological interventions not targeting or not useful for osteoporosis self-management, those that had no clear relationship with osteoporosis, those related to other musculoskeletal conditions, or those not useful for osteoporosis. Articles that did not use mobile apps were excluded, in addition to studies that examined social network platforms and services (such as Telegram, Skype, WhatsApp, or Facebook), emails, and the web. In the same context, we excluded studies that did not use any mobile app or use mobile technologies as an auxiliary tool, namely, by sending SMS text messages to engage patients in certain activities or behaviors.
We searched for
The following information was abstracted from each study: sample size, sample age range, app name, app purpose, app operating platform, study design, intervention period, and major outcome indices. Publication bias of randomized controlled trial (RCT) studies was evaluated using the Cochrane risk of bias (ROB; version 2.0) tool [
The selection, screening, data abstraction, and quality appraisals were performed by 2 reviewers (GA and LH). Any disagreements between the reviewers were resolved through discussion.
Data from 41% (13/32) of studies were pooled in a statistical meta-analysis using meta-essential [
On the basis of the features provided by the apps, a scoring system was created for each app from the web-based market and those included from the research field. The selection of the scoring features stemmed from a combination of related theories. In particular, we followed the Technology Acceptance Model [
Self-management features for both research and web-based apps.
Self-management facet | Web-based market app feature | Research app features |
Socialization |
Networking capabilities Data sharing |
Data sharing or export Communication |
Scheduling |
Reminders Medication plan Diet programs Exercises |
Planning Medication plan Diet programs Exercises |
Warnings |
Fractures Health warnings |
Notifications |
User acceptability and usability |
Visual aids Aesthetic and minimalistic designa Recognition rather than recalla Error preventiona |
Visual aids |
Personalization or adaptation to change | N/Ab |
Chatbot Artificial intelligence |
Performance monitoring | N/A |
Feedback Progress tracking |
Self-care |
Diagnosis |
Diagnosis |
aThese features were selected based on the 10 usability heuristics for user interface design of Nielsen and Mack [
bN/A: not applicable.
Scoring for research apps: (A) Mean score per app available in the literature, with a raw cutoff score of 2.7; apps above the threshold provide a more holistic self-management plan. (B) Selected features with their mean score representing how often they were present in the apps. Features with the highest scores were available in a larger number of apps; features with the lowest scores (ie, chatbot and artificial intelligence) were present in only 1 app [
Web-based apps scores: (A) Mean score per app available in the web-based markets, with a raw cutoff score of 2.7; apps above the threshold provide a more holistic self-management plan. (B) Selected features with their mean score representing how often they were present in the apps. Features with the highest scores were available in more apps, whereas the features with the lowest scores were present in only 2 to 3 apps.
This systematic review was performed based on the recommendations of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement [
The literature search yielded 4185 articles, of which 3906 (93.33%) were screened. After removing duplicates and excluding studies on the basis of their titles and abstracts, 3.02% (118/3906) full texts were assessed for eligibility. In the final stage, 74.6% (88/118) of full-text citations did not meet the inclusion criteria. After completely reviewing the corresponding full-text articles, of the 88 articles, the total number of accepted articles was reduced to 32 (36%), of which 13 (41%) were selected for the meta-analysis. A PRISMA flowchart [
Flow diagrams for the selection of (A) studies and (B) apps.
Research app characteristics.
Author | App name | Sample size (age) | Experiment (participant sample size) | Platforma (private or public) | App purpose (direct or indirect) | Intervention period | Major outcome indices |
Daly et al [ |
PhysiApp-patient portal | 20 (>65 years) | App (20) | Android (public) | Remotely delivers and monitors an individually tailored, home-based multicomponent exercise program (indirectb) | 8 weeks | Feasibility, usability, physical activity enjoyment, changes in lower extremity function, and level of physical activity |
Bhatia et al [ |
Manage My Pain | 246 (mean age 57, SD 15 years) | App (111); no app (135) | Android and iOS (public) | Measures and monitors pain, function, and medication use (indirect) | 92-183 days | Anxiety, depression, pain catastrophizing, satisfaction, daily opioid consumption, engagement |
Cairo et al [ |
Vida app | 127 (>18 years) | App (66); no app (61) | Android and iOS (public) | Improves wellness outcomes for survivors of breast cancer (indirect) | 6 months | Physical activity, diary patterns, fatigue, and depression improvement |
Hauser-Ulrich et al [ |
SELMA-Chatbot | 102 (mean age 43.7 years) | App (59); no app (43) | Android and iOS | Promotes self-management of chronic pain (indirect) | 12 weeks | Pain-related impairment, intention to change behavior, and pain intensity |
Suso-Ribera et al [ |
Pain Monitor | 87 | App (43); no app (44) | N/Ac (private) | Improves existent medical treatments for patients with chronic musculoskeletal pain (indirect) | 4 weeks | Pain severity and interference, fatigue, depressed mood, anxiety, and anger |
Licciardone et al [ |
N/A | 102 (mean age 51 years) | App (52); no app (50) | N/A | Self-management of health‐related quality of life (indirect) | 3 months | Change in the SPADEd cluster score, changes in low back pain intensity, and back‐related disability |
Geerds et al [ |
N/A | 24 (older adults >60 years) | App (24); no app (24) | N/A (private) | Monitors postoperative functional outcome after hip fracture (indirect) | 12 and 18 weeks after surgery | Usability |
Bailey et al [ |
Hinge Health app | 10,264 (mean age 43.6 years) | App (10,264) | N/A (private) | Provides education, sensor-guided exercise therapy, and behavioral health support with one-on-one remote health coaching (indirect) | 12 weeks | Pain measured by the Visual Analog Scale, engagement levels, program completion, program satisfaction, condition-specific pain measures, depression, anxiety, and work productivity |
Ryan et al [ |
Striving app, Boning up | 290 (40-60 years) | App (84); e-book (84); no app (84) | Android and iOS (private) | Provides information and feedback and monitors behavior change (directe) | 12 months | Bone mineral density and trabecular bone scores |
Papi et al [ |
Nymbl | 35 (≥55 years) | App (35) | N/A (private) | Trains balance in the older population (indirect) | 3 weeks for all, with optional follow-up for 3 weeks | Physical activity level and adherence and IPAQf questionnaire |
Sandal et al [ |
selfBack | 51 (mean age 45.5, SD 15.0 years) | App (51) | N/A (private) | Improves self-management of low back pain (indirect) | 6 weeks | Pain-related disability (RMDQg) and multiple self-reported outcomes |
Urena et al [ |
m-SFT | 7 (53-61 years); the system usability was evaluated by 34 health experts (mean age 36.64 years) | App (7) | Android (private) | Easy-to-use tool for a health practitioner to record and assess the physical condition of older adults (indirect) | N/A | Usability questionnaire |
Li et al [ |
Caspar Health App or Website | 31 (≥60 years) | App (15); no app (16) | Android and iOS (public) | Postfracture telerehabilitation (direct) | 3 weeks | Motor performance, functional performance, and fall efficacy; degree of independence in ADLh performance |
Kim et al [ |
Fracture Liaison Service | 60 (>60 years) | App (60) | Android and iOS (public) | Fall prediction and monitoring (direct) | N/A | Usability |
Amorim et al [ |
Fitbit (activity tracker) and IMPACT app | 68 (mean age 58.4, SD 13.4 years) | App (34); no app (34) | Android and iOS (public) | Reduces care seeking, pain, and disability in patients with chronic low back pain after treatment discharge (indirect) | 15 months | Care seeking, pain levels, and activity limitation |
Subasinghe et al [ |
Tap4Bone: MyFitnessPal, Nike Training Club, and QuitBuddy | 35 (mean age 23.1 years) | App (18); no app (17) | Android and iOS (public) | MyFitnessPal is a free calorie counter app that helps people track their diet and exercise; Nike Training Club is a free app comprising >100 full-body workouts; QuitBuddy is a smoking cessation internet-based app (indirect) | 9 weeks | Feasibility and compliance |
Arkkukangas et al [ |
OEP app | 12 (70-83 years) | App (12) | N/A (private) | Fall prevention (indirect) | 6 weeks | Questionnaire and behavior change |
Shebib et al [ |
DCP with sensors | 177 (mean age 43, SD 11 years) | App (113); no app (64) | N/A (private) | Aids self-management by engaging patients, and scales personalized therapy for patient-specific needs (indirect) | 12 weeks | ODIi, Korff Pain, and Korff disability |
Bedson et al [ |
Keele pain recorder | 21 (>18 years) | App (21) | Android (public) | Records pain levels, interference, sleep disturbance, analgesic use, mood, and side effects (indirect) | 28 days | Usability and acceptability |
Hou et al [ |
eHealth | 168 (18-64 years) | app (84); no app (84) | N/A (private) | Telerehabilitation and self-management interventions (indirect) | 3, 6, and 12 months | Disease-specific questionnaire (ODI), Visual Analog Scale to record back pain, measures of mental health and life status, which included the EuroQol 5-Dimension health questionnaire |
Saran et al [ |
N/A | 927 (20-80 years) | App (927) | N/A (private) | Monitors physical activity (indirect) | 1 week | Home physical activity |
Chhabra et al [ |
Snapcare | 93 mean) age 41.4, SD 14.2 years) | App (45); no app (48) | Android (private) | Monitors patient’s daily activity levels and symptomatic profile (indirect) | 12 weeks | Pain and disability |
Jakobsen et al [ |
My Osteoporosis Journey | 18 (50-65 years) | App (18) | Android and iOS (private) | Provides information and usability questionnaires (direct) | 12 weeks | Satisfaction with the app and risk calculation |
Lambert et al [ |
PhysiotherapyExercises | 80 (34-59 years) | App (40); no app (40) | N/A (private) | Home exercise programs (indirect) | 4 weeks | Self-reported exercise adherence, The Patient-Specific Functional Scale, degree of disability, and patient satisfaction with health care service |
Rasche et al [ |
Aachen fall prevention app | 79 (>50 years) | App (79) | Android and iOS (private) | Self-assessment of older patients at risk for ground-level falls (indirect) | 1 year | Objective fall risk and the self-assessed subjective fall risk |
Park et al [ |
|
82 (<25 years; women) | App (36); no app (38) | Android (private) | Provides feedback and records activity and nutrition (direct) | 20 weeks | Bone mineral density, minerals, biochemical markers, food intake diary, knowledge, health belief, and self-efficacy |
Tay et al [ |
Calci-app | 40 (18-25 years) | App (40) | Android and iOS (private) | Usability questionnaires (direct) | 5 days | Dietary calcium intake |
Goodman et al [ |
VDC-app | 109 (18-25 years) | App (59) | iOS (private) | Provides information and feedback and monitors behavior change (direct) | 12 weeks | Vitamin D intake, knowledge, perceptions of vitamin D, blood concentrations of 25(OH)D3 |
Singler et al [ |
AOTrauma’s orthogeriatrics | 920 (health professionals) | App (920) | Android and iOS (public) | Delivers the app to surgeons, trainees, and other health care professionals to measure use and evaluate the impact on patient care (direct) | Web-based one-time evaluation | Rating of app and usability |
aApp is available to the public in app stores, or app is not available to the public in app stores.
bThe study has an indirect relation to osteoporosis.
cN/A: not applicable.
dSPADE: sleep disturbance, pain, anxiety, depression, and low energy or fatigue.
eThe study or app has a direct relation to osteoporosis.
fIPAQ: International Physical Activity Questionnaire.
gRMDQ: Roland-Morris Disability Questionnaire.
hADL: activities of daily living.
iODI: Oswestry Disability Index.
All selected articles were published in journals over the preceding 8 years (2014-2021), with a notable increase in publications since 2017. The publications comprised feasibility studies [
As of May 2021, we found 33 relevant apps for osteoporosis. Most of the apps identified were found in Google Play (16/33, 48%) and Apple stores (13/33, 39%). Approximately 9% (3/33) of apps were available in the Amazon app store, 3% (1/33) in the GetJar app store, and none in the Galaxy app store.
After removing the overlapping apps across stores, 70% (23/33) of unique apps remained (
Web-based app characteristics.
App name | Operating system | Description | Users | Classification |
AACE osteoporosis treatment algorithma | iOS | Provides evidence-based information about the diagnosis, evaluation, and treatment of postmenopausal osteoporosis for endocrinologists, physicians in general, regulatory bodies, health-related organizations, and interested laypersons | Health care professionals | Information and education |
Calcium Proa | Android and iOS | Provides information about calcium, parathyroid, osteoporosis, and vitamin D issues; inputs test results for calcium, parathyroid hormone, and vitamin D; analyzes and graphs tests making them easy to understand; tracking tools show calcium and vitamin D levels over time and provide feedback about bone density status; a risk assessment tool for conditions associated with high blood calcium | Patients | Monitoring, education, and assessment |
Vitamin-D Proa | iOS | Analyzes and graphs current vitamin D levels, calcium levels, calcium versus parathyroid hormone, bone density, and osteoporosis; teaches how to interpret data and graphs; gives personalized suggestions for next steps; suggests what new blood tests may be necessary; gives topics to discuss with the physician | Patients | Assessment, monitoring, and education tool |
Osteoporosis Low Bone Density Weak Bones Diet Helpa | Android | Provides information about the causes, symptoms, treatment, and the type of diet that one should eat to improve bone density | Patients | Information and education |
Bones diseases and treatmentsa | Android | Information about all bone diseases | Patients | Information and education |
My Arthritisa | Android | Keeps track of symptoms and flares; it can also track diet, exercise, pain, sleep, mood, stress; provides paid training courses with videos, guided audio, and expert advice; sets reminders for appointments and medication; access and share medical records from anywhere; learn about community news, current research, and other information | Patients | Monitoring, assessment, and management |
Calcium Calculatora (by BC Dairy) | Android | Tool to assess, compare, and plan to introduce enough calcium in daily food | Patients | Monitoring, assessment, and education |
Osteoporosisa (by AZoMedical) | iOS | Provides regularly updated information and news on osteoporosis | Professionals and patients | News |
My Osteoporosis Manager | iOS | Capture detailed information regarding user’s health in a digital journal; manage medications and treatments; track osteo-specific symptoms and side effects feedback as easy-to-understand charts that record test results and medication adherence; access patient education materials; share information with a health care provider | Patients | Monitoring, assessment, and management |
Osteoporosis (by Focus Media) | Android | Animated videos for learning about osteoporosis disease | Patients | Information and education |
Osteoporosis disease | Android | Information about causes, symptoms, treatment, and the type of diet that one should eat to improve bone density | Patients | Information and education |
Osteoporosis (by health care tips) | Android | Information and education | Patients | Information and education |
Postmenopausal Osteoporosis | Android | Helps in understanding the disease condition through animated videos; it gives an insight into the structure and formation of bones, changes with age, and hormonal levels, particularly during menopause; it also provides information on the onset of osteoporosis, measurement of bone density, treatment, and self-help guidelines | Patients | Information and education |
Osteoporosis (by personal remedies) | Android | Comprehensive and actionable nutrition guidelines for how to deal with osteoporosis; recipes, food suggestions, alternative therapies, and remedies | Patients | Information and education |
Calcium Supplements | Android | Information about calcium supplements, including who should take them, their health benefits, and potential risks | Patients | Information and education |
Osteoporosis AR | Android | Demonstrates a different fictional patient profile using the augmented reality technique that illustrates patient insights, symptoms they are experiencing, and how these agonizing symptoms affect patient’s quality of life | Patients | Information and education |
Cure for Osteoporosis | Android | Information about raloxifene | Patients | Information |
Osteoporosis Risk Calculator | Android | A risk check that calculates whether the user is at risk of fracture or osteoporosis | Patients | Measurement and assessment tool |
Hip Fracture Risk Calculator | iOS | Calculates whether the user is at risk of fracture or osteoporosis based on patient demographics | Patients | Measurement and assessment tool |
Calcium Calculator | iOS | Calculate calcium intake daily | Patients | Measurement tool |
My Osteo-Team | Android and iOS | A social network and support group for those living with osteoporosis; users can acquire practical tips to manage their life with osteoporosis and insights about treatment or therapies | Patients | Social network |
Low back pain exercise | Android | Exercises to reduce low back pain | Patients | Information and education |
The spine app | Android | Information about back pain | Patients | Information and education |
Fracture | Android | Information about fracture prevention | Patients | Information and education |
aRanked according to their rating rates, with the highest-ranking rates on the top, and vice versa. The other apps did not have any ratings or reviews. The ranking rate did not reflect the number of times the app was downloaded, and there was no direct relationship between the number of times an app was downloaded and its rating.
The results of app ranking are presented in
Similarly, apps in the web-based markets that attained large scores, such as
For the ROB In Nonrandomized Studies of Interventions assessment of nonrandomized clinical trials, 75% (12/16) of studies were at critical ROB, 13% (2/16) at serious risk, and 6% (1/16) at moderate ROB. Among the 13 RCTs assessed using the Cochrane ROB (version 2.0) tool [
(A) Risk of bias (ROB) assessment for randomized (ROB 2.0) and (B) nonrandomized (ROBIN-I) trials. The studies above the horizontal black line are above the app's cutoff score (2.7) and vice versa [
Approximately 6% (2/32) of studies measured BMD T score at baseline and after 20 weeks [
Forest plots of Hedges g effect size (95% CI) from individual studies before and after using the app showing changes in (A) bone mineral density (BMD) T score, (B) vitamin D intake (µg per day), (C) calcium intake (µg per day), and (D) physical activity (hours per week) [
Approximately 6% (2/32) of studies compared the average (µg per day) Vitamin D intake before and after app intervention [
Approximately 6% (2/32) of studies measured the differences in the average mg per day of calcium intake [
Approximately 6% (2/32) of studies measured the average number of hours per week of physical activities before and after 15 months [
Approximately 6% (2/32) of studies evaluated physical function before and after 4 weeks [
Forest plots of Hedges g effect sizes (95% CI) from individual studies before and after using the app showing changes in (A) physical function, (B) well-being, (C) fatigue, and (D) anxiety [
Approximately 6% (2/32) of studies observed changes in well-being from baseline after 12 weeks [
Approximately 6% (2/32) of studies measured changes in anxiety and fatigue at baseline and after 3 months [
Approximately 25% (8/32) of studies recorded pain intensity before and after initiation of the interventions [
Forest plots of Hedges g effect sizes (95% CI) from individual studies before and after using the app showing changes in (A) pain intensity and (B) disability [
Approximately 19% (6/32) of studies evaluated disability [
The focus of this review was placed on a systematic examination of the available literature on mHealth technologies and apps that can support the self-management of osteoporosis and decision-making for young and older adults. Although some of these apps showed promising results for the use of mHealth technologies in osteoporosis management, there is a lack of evidence in the research to prove the effectiveness of these apps, as validation studies have not been run on all the included apps.
Most (39/52, 75% apps) of the analyzed mHealth apps did not conduct premarket prospective multicenter RCTs. This might be because of the elevated cost of the trials and the long time required to recruit patients [
From the scoring system created in
Our meta-analysis showed that by using the apps, pain scores were significantly reduced in 25% (8/32) of studies [
According to our results, app use had no impact on the physical activity of app users. The meta-analysis also revealed that digital health interventions had no significant impact on the daily intake of calcium and vitamin D or on the BMD trabecular score. It is important to note that patients’ adherence to and compliance with the use of mHealth apps are pivotal in ensuring improved health outcomes and successful intervention programs. Some studies reported a high dropout rate in patients who found the intervention boring, time consuming [
The data yielded by the meta-analysis demonstrated that using the app had no significant impact on well-being, anxiety, and fatigue scores. This might be explained by the fact that patients self-reported these outcomes in all the evaluated studies without any validation [
A review of apps related to osteoporosis in the web-based marketplace resulted in 23 apps. Most of these apps provided information and education, such as disease definition, common symptoms, and suggested exercises to strengthen the bones and enhance physical activity or instructions on healthy nutrition. In addition, none of these available apps addressed the management of the disease after fracture, although fracture is the main complication of osteoporosis. Although osteoporosis is widespread in society, especially among adults, our findings revealed that the number of people who downloaded these osteoporosis-related apps is very limited, as it can barely reach 1000 downloads. Some of these apps did not report any downloads at all. This also indicates that patients or clinicians are hesitant toward the adoption of these new technologies. Unfortunately, these results revealed the poor contribution of research and development toward the field of mHealth apps designed for osteoporosis management and the untrustworthy content that does not have any strong reference [
Information privacy is an important issue in mHealth apps because of the sensitive nature of information gathered from users [
Our findings show that only one of the identified studies [
This review shows that mHealth apps that use self-management support principles in primary care have the potential to have a positive effect on the management of chronic diseases. However, there is reluctance in the adoption of these digital technologies in health care. The main obstacles delaying the integration of these technological tools in osteoporosis care could be summarized as (1) weak or no involvement of health care professionals in the design process, (2) reluctance of clinicians who believe that mHealth apps might replace them, (3) lack of reliable tools and strict regulations, (4) privacy and security concerns, (5) data availability and visualization, (6) inconsistent data collection standards, (7) difficulties in acquiring and analyzing data, and (8) low retention rates of participants (
DRsa and CRsb for overcoming the identified limitations or barriers in digital health technologies for osteoporosis.
Identified limitation or barrier and related aspect | Recommendation | ||
|
|||
|
Design perspective |
DR1: involve all the stakeholders in all the stages of user requirements, design, and development using a participatory design approach (cocreation) |
|
|
Clinical perspective |
CR1: active participation in the design, development, and testing stages |
|
|
|||
|
Clinical perspective |
CR1: adopt mHealth technologies in daily practices and in clinical care (measurement, assessment, and recording data) CR2: recommend trustworthy apps to their patients CR3: use mHealth apps to effectively communicate with patients and other health care professionals through the integration of wearables and IoTd |
|
|
|||
|
Design perspective |
DR1: use adaptive learning algorithms (eg, AIe and machine or deep learning) in the app to make more personalized recommendations and treatments DR2: incorporate clinically validated monitoring, measurement, and assessment tools in the designed app |
|
|
Clinical perspective |
CR1: evaluate mHealth measurement and assessment tools by concerned clinical experts before disseminating them to public |
|
|
|||
|
Design perspective |
DR1: implement stringent security regulations (eg, GDPRf [ |
|
|
|||
|
Design perspective |
DR1: allow patients to access their data (GDPR enforcement in design) DR2: generate feedback and plans (for diet and exercises) based on the gathered data to keep patients engaged and motivated |
|
|
|||
|
Design perspective |
DR1: use passive and active gathering of data (medication, symptoms, nutrition management, and physical exercising), in addition to the data gathered from any wearables or IoT sensors |
|
|
Clinical perspective |
CR1: combine conventional clinical assessment with the app assessment |
|
|
|||
|
Design perspective |
DR1: apply AI-based techniques that help with the prediction, diagnosis, and treatment or management of diseases |
|
|
|||
|
Design perspective |
DR1: provide valuable feedback to the user DR2: use simple and straightforward interfaces DR3: continuously update users’ data DR4: offer financial incentives for healthy habit changes |
aDR: design-related recommendation.
bCR: clinical recommendation.
cmHealth: mobile health.
dIoT: Internet of Things.
eAI: artificial intelligence.
fGDPR: General Data Protection Regulation.
To overcome these obstacles, we propose design-related and clinical recommendations for mHealth apps to support patients at risk of or diagnosed with osteoporosis in self-management and involve them in decision-making regarding treatment and intervention options with clinicians. These guidelines are not only limited to apps targeting osteoporosis self-management but can also be applied to any chronic disease self-management app.
Before creating an app, we should emphasize the role of end users, including patients and health professionals, in the development process. Users should be involved at various points and levels in the design process to improve their understanding of their needs, requirements, interactions, and appreciations before, during, and after developing the app. This co-design will ensure that the developed app meets end user purposes, leading to more effective results [
An enhanced and intelligent version of the mHealth app can perform long-term management of osteoporosis through internet-based coaching using AI and big data analysis. In addition to health care professionals, AI can play an important role in the decision-making process and in the entire self-management process of osteoporosis. Conventional systems used for processing health data are less accurate and lack convergence compared with AI-supported systems [
Owing to the significant advances and progress in AI in the past few years, chatbots have been gaining momentum in the eHealth world. Therefore, we believe that a bot framework can be incorporated with virtual reality technology and low-cost Internet of Things to create a user engagement schema for long-term monitoring of osteoporosis, where the patient will be active and maintain an improved quality of life.
An innovative technological tool (mHealth app) should offer an integrated platform for informed healthy living indoors or outdoors to assist patients with osteoporosis (or at risk) in different aspects of life, including physical activity, nutrition, medication intake, fall prevention, emotional wellness, and socialization. The design of such tools could include monitoring, combining both passive (via the interaction with smartphone or smartwatch or wearables) and active gathering of data (eg, about medication, nutrition management, and physical exercising). Then, on the basis of the gathered data, AI-driven data analysis processes could be involved in providing personalized feedback to the patient and informing the related physician, guiding personalized recommendations and interventions for osteoporosis risk assessment [
mHealth app developers must ensure that collected user data are secured to maintain the integrity, availability, confidentiality, and resilience of the data [
The success and effectiveness of any mHealth app intervention are directly related to user retention [
As patients increasingly turn to mHealth apps and devices, clinicians must consider the value of these apps and embrace them to deliver enhanced care. They should adopt more mHealth technologies in their daily practices or workflows and integrate the data into electronic medical records. However, physicians should refrain from recommending apps that have been created without the involvement of medical experts or appropriate testing validation, especially if claims made by app developers are fraudulent. To ensure that their requirements are met and to deliver better outcomes, physicians should actively participate in the design, development, and testing of these mHealth apps [
mHealth apps create a sense of partnership between patients and health care professionals by allowing patients to play a more active role in their health care. Moreover, digital health will improve patients’ engagement with their treatment, something that physicians have previously struggled to do between visits [
Despite this in-depth analysis, some limitations can be identified in this review. In particular, we refrained from excluding studies based on certain quality criteria, such as study design or sample size, which resulted in large variations in the measurements of outcomes. Moreover, there is a lack of apps that only target osteoporosis; therefore, we included apps that we thought were
Given the identified lack of effective mHealth apps with a holistic approach to osteoporosis self-management, this review holds the potential to bridge this gap by proposing a technological tool that goes beyond apps that simply provide information about osteoporosis and creates an individualized care management plan that goes beyond clinical measures. The latter perspective extends the view of mHealth apps from the initial focus on promoting specific behavior, such as healthy nutrition, physical activity, or adherence to medications, to patients’ engagement and empowerment. Moreover, it strengthens collaboration between patients and caregivers by not limiting it to health institutions. In view of the vast quantity of mHealth apps available, it is important for app developers and researchers to identify the proper needs of patients with osteoporosis, adopting a cocreation strategy to create more patient-centered and effective disease management solutions.
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist, detailed search terms, and sensitivity analysis.
artificial intelligence
bone mineral density
mobile health
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
randomized controlled trial
risk of bias
This work was supported by Khalifa University of Science and Technology under award number CIRA-2020-031.
None declared.