The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index; none of the 37 sources its claims cite is checked for support yet.
What is the clinical value of mHealth for patients?
Open accessAbstract
Despite growing interest from both patients and healthcare providers, there is little clinical guidance on how mobile apps should be utilized to add value to patient care. We categorize apps according to their functionality (e.g. preventative behavior change, digital self-management of a specific condition, diagnostic) and discuss evidence for effectiveness from published systematic reviews and meta-analyses and the relevance to patient care. We discuss the limitations of the current literature describing clinical outcomes from mHealth apps, what FDA clearance means now (510(k)/de novo FDA clearance) and in the future. We discuss data security and privacy as a major concern for patients when using mHealth apps. Patients are often not involved in the development of mobile health guidelines, and professionals’ views regarding high-quality health apps may not reflect patients’ views. We discuss efforts to develop guidelines for the development of safe and effective mHealth apps in the US and elsewhere and the role of independent app reviews sites in identifying mHealth apps for patient care. There are only a small number of clinical scenarios where published evidence suggests that mHealth apps may improve patient outcomes.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review categorizes mHealth apps by function and assesses the clinical evidence for their value to patients. It concludes that evidence supports use in only a small number of defined clinical scenarios, with major gaps in long-term outcomes, evidence quality, regulation, and data privacy.
Key findings
- Evidence for clinical benefit is limited to a handful of scenarios. For example, an app screening patient-reported outcomes for lung cancer recurrence improved median survival to 23 months vs 14.8 months with standard imaging follow-up (HR 0.62, 95% CI 0.39–0.995, P = 0.048).
- Diagnostic symptom-checker apps gave appropriate emergency triage advice in 80% of cases but had only 34% diagnostic accuracy and appropriate non-emergent triage advice in 55%. A separate meta-analysis of apps screening photographs or sensor data reported pooled sensitivity of 82% (95% CI 0.56–0.94) and specificity of 89% (95% CI 0.70–0.97).
- Behavior-change and digital therapeutic apps show some benefits: a meta-analysis found BMI reduction of 0.43 kg/m² (95% CI −0.74 to −0.13), and an updated meta-analysis of 66 RCTs found improved depression, anxiety, and stress outcomes vs controls, with preliminary evidence showing no difference vs face-to-face therapy.
- Medication adherence apps are numerous but poorly evidenced: only 4 of 805 included apps had associated published evidence of clinical outcomes. An educational app for knee pain improved actual disease-related knowledge by 52% and perceived knowledge by 22%.
Methods
- Design: narrative review/perspective that categorizes mHealth apps by functionality and discusses evidence, regulation, and implementation challenges.
- Data: published systematic reviews, meta-analyses, randomized controlled trials, regulatory documents, and guideline efforts; the authors note a PubMed search for “mHealth” returns more than 30,000 hits.
- Main analysis: thematic synthesis across four functional categories—diagnostics/clinical decision making, behavior change, digital therapeutics, and disease-related education—plus discussion of FDA clearance, data security/privacy, and app-review/guideline initiatives.
Limitations
- Stated: most evidence comes from short-term studies, long-term value remains unclear, and variations in language and cultural setting may limit generalizability. The mHealth literature is highly variable, few studies are registered on clinicaltrials.gov, many studied apps are unavailable on app stores, and retrospective analyses risk selection bias, commercial bias, and outdated software versions.
- Stated: usability and acceptability are often not evaluated, and there is no evidence-based standardized approach for clinicians to counsel patients on mHealth risks such as data privacy.
- Evident: as a narrative review, it does not report a systematic search strategy, inclusion criteria, or formal risk-of-bias assessment, so its conclusions rely on selected examples and syntheses rather than a comprehensive systematic review.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
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Verification
Claims the paper makes
0 of 37 cited sources checked · 0 supported · 0 flagged
- Findingnone given
Another common limitation of the mHealth literature is a lack of published evidence around the usability and acceptability of individual mHealth apps for different patient populations.
“Another common limitation of the mHealth literature is a lack of published evidence around the usability and acceptability of individual mHealth apps for different patient populations.”
Quote in the paperNot a substantial claim - Findingnone given
The majority of the evidence included in this review is collected from clinical studies performed over short time periods.
“The majority of the evidence included in this review is collected from clinical studies performed over short time periods.”
Quote in the paperSubstantial claim - Premisecitation
Only 4 out of 805 medication adherence apps included had associated published evidence of clinical outcomes.
“A systematic review noted that only 4 out of 805 medication adherence apps included had associated published evidence of clinical outcomes.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [15] Ahmed, I. et al. Medication adherence apps: review and content analysis. JMIR mHealth uHealth. https://doi.org/10.2196/mhealth.6432 (2018). source
- Premisecitation
Benefits have been seen with the use of behavior change apps to drive improvements in glycemic control in people with diabetes through improved adherence to treatment protocols.
“Benefits have also been seen with the use of behavior change apps to drive improvements in glycemic control in people with diabetes through improved adherence to treatment protocols.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11] Wu, Y. et al. Mobile app-based interventions to support diabetes self-management: a systematic review of randomized controlled trials to identify functions associated with glycemic efficacy. JMIR mHealth uHealth. https://doi.org/10.2196/mhealth.6522 (2017). source
- Premisecitation
Interactive symptom checkers are the most frequently investigated category of diagnostic app.
“a recent systematic review demonstrated that interactive symptom checkers are the most frequently investigated category of diagnostic app.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [3] Millenson, M. L., Baldwin, J. L., Zipperer, L. & Singh, H. Beyond Dr. google: the evidence about consumer-facing, digital tools for diagnosis. Diagnosis https://doi.org/10.1515/dx-2018-0009 (2017). source
- Premisecitation
A recent review identified 704 apps on the app store utilizing behavioral strategies to enhance medication adherence through alerts, reminders, and logs.
“A recent review identified 704 apps on the app store utilizing behavioral strategies to enhance medication adherence through alerts, reminders, and logs.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [14] Park, J. Y. E., Li, J., Howren, A., Tsao, N. W. & De Vera, M. Mobile phone apps targeting medication adherence: quality assessment and content analysis of user reviews. JMIR mHealth uHealth 7, e11919 (2019). source
- Premisecitation
An app designed to screen patient reported outcomes for signs of disease recurrence to allow for early re-intervention in previously treated lung cancer patients improved median survival compared to optimized standard imaging follow-up (23 months vs 14.8 months; HR 0.62, 95% CI 0.39-0.995, P = 0.048).
“For example, an app designed to screen patient reported outcomes for signs of disease recurrence to allow for early re-intervention in previously treated lung cancer patients improved median survival compared to optimized standard imaging follow-up (23 months in the experimental arm vs 14.8 months in the control arm (HR 0.62, 95% CI 0.39–0.995, P = 0.048)).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [7] Denis, F. et al. Randomized trial comparing a web-mediated follow-up with routine surveillance in lung cancer patients. J. Natl. Cancer Inst. https://doi.org/10.1093/jnci/djx029 (2017). source
- Premisecitation
Pooled diagnostic sensitivity for apps screening photographs or analyzing movements was 82% (95% CI 0.56-0.94).
“A pooled estimate of diagnostic sensitivity for such apps was reported as 82% (95% CI 0.56–0.94) and pooled specificity as 89% (95% CI 0.70–0.97).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [4] Semigran, H. L., Linder, J. A., Gidengil, C. & Mehrotra, A. Evaluation of symptom checkers for self diagnosis and triage: Audit study. BMJ 351, h3480 (2015). source
- Premisecitation
Diagnostic accuracy of symptom checker apps was only 34%.
“However diagnostic accuracy was only 34% and triage advice was considered appropriate in only 55% of non-emergent cases.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [4] Semigran, H. L., Linder, J. A., Gidengil, C. & Mehrotra, A. Evaluation of symptom checkers for self diagnosis and triage: Audit study. BMJ 351, h3480 (2015). source
- Premisecitation
Diagnostic symptom checker apps targeted at specific symptoms such as hand or knee pain frequently deliver inaccurate advice.
“Diagnostic symptom checker apps targeted at specific symptoms such as hand or knee pain have been shown to frequently deliver inaccurate advice.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [3] Millenson, M. L., Baldwin, J. L., Zipperer, L. & Singh, H. Beyond Dr. google: the evidence about consumer-facing, digital tools for diagnosis. Diagnosis https://doi.org/10.1515/dx-2018-0009 (2017). source
- Premisecitation
Triage advice was considered appropriate in only 55% of non-emergent cases.
“However diagnostic accuracy was only 34% and triage advice was considered appropriate in only 55% of non-emergent cases.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [4] Semigran, H. L., Linder, J. A., Gidengil, C. & Mehrotra, A. Evaluation of symptom checkers for self diagnosis and triage: Audit study. BMJ 351, h3480 (2015). source
- Premisecitation
A 2017 systematic review identified 30 manuscripts investigating 35 apps most commonly intended to screen photographs for melanoma or diagnose tremor through analyses of movements.
“A 2017 systematic review identified 30 manuscripts investigating 35 apps most commonly intended to screen photographs for melanoma or diagnose tremor through analyses of movements.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [5] Buechi, R. et al. Evidence assessing the diagnostic performance of medical smartphone apps: a systematic review and exploratory meta-analysis. BMJ Open 7, 1–8 (2017). source
- Premisecitation
An audit of 23 prominent symptom checker apps in a US clinic found that appropriate triage advice was given in 80% of emergency cases.
“An audit of 23 prominent symptom checker apps in a US clinic found that appropriate triage advice was given in 80% of emergency cases, which is comparable to that seen from junior doctors and senior nurses.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [4] Semigran, H. L., Linder, J. A., Gidengil, C. & Mehrotra, A. Evaluation of symptom checkers for self diagnosis and triage: Audit study. BMJ 351, h3480 (2015). source
- Interpretationnone given
There are only a small number of clinical scenarios where published evidence suggests that mHealth apps may improve patient outcomes.
“There are only a small number of clinical scenarios where published evidence suggests that mHealth apps may improve patient outcomes.”
Quote in the paperNot a substantial claim - Interpretationnone given
Long-term outcomes with use of behavior change apps are unknown.
“However the challenge is to achieve sustained improvements in clinical parameters and the long-term outcomes with use of these devices is unknown.”
Quote in the paperSubstantial claim - Premisecitation
Pooled specificity for such apps was 89% (95% CI 0.70-0.97).
“A pooled estimate of diagnostic sensitivity for such apps was reported as 82% (95% CI 0.56–0.94) and pooled specificity as 89% (95% CI 0.70–0.97).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [4] Semigran, H. L., Linder, J. A., Gidengil, C. & Mehrotra, A. Evaluation of symptom checkers for self diagnosis and triage: Audit study. BMJ 351, h3480 (2015). source
- Premisecitation
A systematic review and meta-analysis demonstrated reductions in BMI of 0.43 kg/m2 (95% CI −0.74 to −0.13) with mobile apps to improve weight loss and physical activity outcomes.
“A systematic review and meta-analysis of published studies investigating the effect of mobile apps to improve weight loss and physical activity outcomes demonstrated reductions in BMI of 0.43 kg/m2 (95% CI −0.74 to −0.13) with much greater weight loss seen when the devices were used optimally.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [10] Mateo, G. F., Granado-Font, E., Ferré-Grau, C. & Montaña-Carreras, X. Mobile phone apps to promote weight loss and increase physical activity: a systematic review and meta-analysis. J. Med. Internet Res. https://doi.org/10.2196/jmir.4836 (2015). source
- Interpretationreasoning
The data from retrospective analyses of patient-generated outcomes can be difficult for clinicians to interpret and apply in a clinical setting as users of the app are generally self-selected and their clinical outcomes may not represent the wider patient population.
“The data from such studies can be difficult for clinicians to interpret and apply in a clinical setting as users of the app are generally self-selected and their clinical outcomes may not represent the wider patient population.”
Quote in the paperSubstantial claim - Premisecitation
A recent systematic review identified 23 different published criteria for assessment of mHealth apps.
“A recent systematic review identified 23 different published criteria for assessment of mHealth apps.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [38] Nouri, R., R. Niakan Kalhori, S., Ghazisaeedi, M., Marchand, G. & Yasini, M. Criteria for assessing the quality of mHealth apps: a systematic review. J. Am. Med. Informatics Assoc. https://doi.org/10.1093/jamia/ocy050 (2018). source
- Premisecitation
User compliance is closely linked to clinical outcomes for mHealth apps designed to stimulate behavior change.
“Such studies are particularly important for mHealth apps that are designed to stimulate behavior change because user compliance is closely linked to clinical outcomes.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [35] Fu, H. N. et al. Influence of patient characteristics and psychological needs on diabetes mobile app usability in adults with type 1 or type 2 diabetes: crossover randomized trial. JMIR Diabetes. https://doi.org/10.2196/11462 (2019). source
- Interpretationreasoning
There is preliminary evidence available to demonstrate the value of mHealth apps to patients in a growing number of defined clinical scenarios.
“In summary there is preliminary evidence available to demonstrate the value of mHealth apps to patients in a growing number of defined clinical scenarios.”
Quote in the paperSubstantial claim - Premisecitation
The same educational app improved perceived knowledge by 22%.
“The app improved the level of actual disease-related knowledge at clinical attendance by 52% and the perceived knowledge by 22%.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [25] Timmers, T. et al. Assessing the efficacy of an educational smartphone or tablet app with subdivided and interactive content to increase patients’ medical knowledge: randomized controlled trial. JMIR mHealth uHealth 6, e10742 (2018). source
- Interpretationreasoning
The long-term value to patients of mHealth interventions for behavior change apps that address chronic conditions remains unclear.
“This is important to note particularly when considering the clinical value of mHealth interventions for behavior change apps that address chronic conditions, where the long-term value to patients remains unclear.”
Quote in the paperSubstantial claim - Premisecitation
Preliminary evidence from this analysis did not show any difference in clinical outcomes with mHealth interventions in head to head trials with face-to-face delivery of therapies.
“Preliminary evidence from this analysis did not show any difference in clinical outcomes with mHealth interventions in head to head trials with face-to-face delivery of therapies.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [19] Linardon, J., Cuijpers, P., Carlbring, P., Messer, M. & Fuller-Tyszkiewicz, M. The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials. World Psychiatry 18, 325–336 (2019). source
- Premisecitation
Popularity on the app store often does not correlate with quality of evidence.
“Clinicians should evaluate the evidence for clinical outcomes for each digital therapeutic as popularity on the app store often does not correlate with quality of evidence.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [24] Wyatt, J. C. How can clinicians, specialty societies and others evaluate and improve the quality of apps for patient use? BMC medicine. 16, 225 (2018). source
- Premisecitation
Current literature suggests that user factors such as high levels of engagement with an mHealth app are frequently associated with better outcomes.
“Current literature suggests that user factors such as a high levels of engagement with an mHealth app are frequently associated with better outcomes and clinicians may be able to support patients by setting goals that are achievable for the individual in a defined time frame with scheduled follow-up.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [43] Lunde, P., Nilsson, B. B., Bergland, A., Kværner, K. J. & Bye, A. The effectiveness of smartphone apps for lifestyle improvement in noncommunicable diseases: systematic review and meta-analyses. J. Med. Internet Res. https://doi.org/10.2196/jmir.9751 (2018). source
- Premisecitation
Many apps popular with patients are not seen as high quality by clinicians.
“Furthermore, many apps popular with patients are not seen as high quality by clinicians.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [32] Singh, K. et al. Many mobile health apps target high-need, high-cost populations, but gaps remain. Health Aff. https://doi.org/10.1377/hlthaff.2016.0578 (2016). source
- cites [39] Singh, K. et al. Patients’ and nephrologists’ evaluation of patient-facing smartphone apps for CKD. Clin. J. Am. Soc. Nephrol. https://doi.org/10.2215/CJN.10370818 (2019). source
- Premisecitation
An educational app with structured interactive content improved the level of actual disease-related knowledge at clinical attendance by 52% in patients with knee pain.
“The app improved the level of actual disease-related knowledge at clinical attendance by 52% and the perceived knowledge by 22%.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [25] Timmers, T. et al. Assessing the efficacy of an educational smartphone or tablet app with subdivided and interactive content to increase patients’ medical knowledge: randomized controlled trial. JMIR mHealth uHealth 6, e10742 (2018). source
- It may state two separate claims as one. 12
- Probably not a substantial claim. 4
It may state two separate claims as one.
Findingown dataA PubMed database search for the term "mHealth" returns more than 30,000 hits; however, our analysis identified only a handful of clinical scenarios where use of mHealth apps is supported by the highest levels of evidence.
“A PubMed database search for the term “mHealth” returns more than 30,000 hits; however, our analysis identified only a handful of clinical scenarios where use of mHealth apps is supported by the highest levels of evidence.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
PremisecitationHigh-quality evidence from multicenter randomized controlled trials has recently emerged demonstrating the potential of app-based interventions to improve treatment adherence in the management of hypertension and clinical outcomes from enhanced perioperative care after elective abdominal surgery.
“High-quality evidence from multicenter randomized controlled trials has recently emerged demonstrating the potential of app-based interventions to improve treatment adherence in the management of hypertension and clinical outcomes from enhanced perioperative care after elective abdominal surgery.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [16] van der Meij, E. et al. Personalised perioperative care by e-health after intermediate-grade abdominal surgery: a multicentre, single-blind, randomised, placebo-controlled trial. Lancet. https://doi.org/10.1016/S0140-6736(18)31113-9 (2018). source
- cites [17] Morawski, K. et al. Association of a Smartphone Application With Medication Adherence and Blood Pressure Control. JAMA Intern. Med. https://doi.org/10.1001/jamainternmed.2018.0447 (2018). source
Probably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationThe World Health Organization advises that digital diagnostic platforms may be valuable for patients in low- and middle-income countries where expert clinical advice is difficult to access.
“The World Health Organization already advises that digital diagnostic platforms may be valuable for patients in low- and middle-income countries where expert clinical advice is difficult to access.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [8] Rowe, A. K. et al. Effectiveness of strategies to improve health-care provider practices in low-income and middle-income countries: a systematic review. Lancet Glob. Health https://doi.org/10.1016/S2214-109X(18)30398-X (2018). source
It may state two separate claims as one.
PremisecitationCommon reasons for non-adherence that can be addressed through an app include forgetfulness, lack of understanding of side effects, and perceptions of lack of efficacy.
“Common reasons for non-adherence that can be addressed through an app include forgetfulness, lack of understanding of side effects, and perceptions of lack of efficacy.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [13] Atinga, R. A., Yarney, L. & Gavu, N. M. Factors influencing long-term medication non-adherence among diabetes and hypertensive patients in Ghana: a qualitative investigation. PLoS ONE. https://doi.org/10.1371/journal.pone.0193995 (2018). source
It may state two separate claims as one.
PremisecitationSymptom checker apps are often risk averse and may lead to unnecessary consultations in the non-emergency setting.
“Published evidence suggests that symptom checker apps are often risk averse and may lead to unnecessary consultations in the non-emergency setting.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [3] Millenson, M. L., Baldwin, J. L., Zipperer, L. & Singh, H. Beyond Dr. google: the evidence about consumer-facing, digital tools for diagnosis. Diagnosis https://doi.org/10.1515/dx-2018-0009 (2017). source
Probably not a substantial claim.
PremisecitationCurrent evidence is not at a level to suggest that apps screening photographs could replace a clinical consultation.
“However, experts have highlighted that current evidence is not at a level to suggest that apps screening photographs could replace a clinical consultation.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [6] The Lancet Oncology. Digital oncology apps: revolution or evolution? Lancet Oncol. 19, 999 (2018). source
It may state two separate claims as one.
PremisecitationThere was positive feedback from patients on features such as customized medication regimen details and reminders; however, there were frequent technical difficulties experienced through glitches, confusing app navigation and dosage scheduling, and reminder setup inflexibility.
“There was positive feedback from patients on features such as customized medication regimen details and reminders; however, there were frequent technical difficulties experienced through glitches, confusing app navigation and dosage scheduling, and reminder setup inflexibility.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [14] Park, J. Y. E., Li, J., Howren, A., Tsao, N. W. & De Vera, M. Mobile phone apps targeting medication adherence: quality assessment and content analysis of user reviews. JMIR mHealth uHealth 7, e11919 (2019). source
Probably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationLong-term non-adherence with prescribed pharmacological treatments for chronic illnesses is estimated to be as high as 50%.
“App-based behavior change interventions are also available to address long-term non-adherence with prescribed pharmacological treatments for chronic illnesses, which is estimated to be as high as 50%.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [12] Zaugg, V., Korb-Savoldelli, V., Durieux, P. & Sabatier, B. Providing physicians with feedback on medication adherence for people with chronic diseases taking long-term medication. Cochrane Database Syst. Rev. https://doi.org/10.1002/14651858.CD012042.pub2 (2018). source
It may state two separate claims as one.
PremisecitationmHealth tools have the potential to be a trusted source of relevant treatment information associated with significant improvements in patient reported quality of life.
“In these situations mHealth tools have the potential to be a trusted source of relevant treatment information associated with significant improvements in patient reported quality of life.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [31] Larson, J. L., Rosen, A. B. & Wilson, F. A. The effect of telehealth interventions on quality of life of cancer patients: a systematic review and meta-analysis. Telemed. e-Health. https://doi.org/10.1089/tmj.2017.0112 (2018). source
It may state two separate claims as one.
PremisecitationRetrospective analyses of patient reported outcomes may suffer from data manipulation that may favor reporting of positive outcomes that support commercial objectives over negative findings, leading to significant bias.
“Retrospective analyses of patient reported outcomes may also suffer from data manipulation that may favor reporting of positive outcomes that support commercial objectives over negative findings. This can lead to significant bias.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [36] Polis, C. B. Published analysis of contraceptive effectiveness of Daysy and DaysyView app is fatally flawed. Reprod. Health. https://doi.org/10.1186/s12978-018-0560-1 (2018). source
It may state two separate claims as one.
PremisecitationThere are thousands of health-related apps available for download from the app store but only a tiny fraction are appropriate for clinical use.
“There are thousands of health-related apps available for download from the app store but only a tiny fraction are appropriate for clinical use.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [15] Ahmed, I. et al. Medication adherence apps: review and content analysis. JMIR mHealth uHealth. https://doi.org/10.2196/mhealth.6432 (2018). source
- cites [37] Byambasuren, O., Sanders, S., Beller, E. & Glasziou, P. Prescribable mHealth apps identified from an overview of systematic reviews. npj Digit. Med. https://doi.org/10.1038/s41746-018-0021-9 (2018). source
It may state two separate claims as one.
PremisecitationThe quality of the mHealth literature is highly variable with few studies registered on clincaltrials.gov and many of the apps studied not available on the iOS or Android app store.
“The quality of the mHealth literature is highly variable with few studies registered on clincaltrials.gov and many of the apps studied not available on the iOS or Android app store.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [32] Singh, K. et al. Many mobile health apps target high-need, high-cost populations, but gaps remain. Health Aff. https://doi.org/10.1377/hlthaff.2016.0578 (2016). source
It may state two separate claims as one.
PremisecitationA systematic review of eight randomized controlled trials (n = 1794) investigating psychological outcomes from the use of mHealth apps to manage depression, chronic pain acceptance, insomnia severity, stress or PTSD symptoms showed variations in effect sizes (d = −0.13 to 1.83; 0.03-1.44) with higher engagement associated with improved outcomes.
“A systematic review of eight randomized controlled trials (n = 1794) investigating psychological outcomes from the use of mHealth apps to manage depression, chronic pain acceptance, insomnia severity, stress or PTSD symptoms showed variations in effect sizes (d = −0.13 to 1.83; 0.03–1.44) with higher engagement associated with improved outcomes.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [18] Rathbone, A. L., Clarry, L. & Prescott, J. Assessing the efficacy of mobile health apps using the basic principles of cognitive behavioral therapy: systematic review. J. Med. Internet Res. https://doi.org/10.2196/jmir.8598 (2017). source
It may state two separate claims as one.
PremisecitationAn updated meta-analysis from October 2019 identified 66 randomized controlled trials and demonstrated significantly improved clinical outcomes for management of depression, anxiety, and stress with mHealth apps vs control groups, with associated improvements in quality of life.
“An updated meta-analysis from October 2019 identified 66 randomized controlled trials and demonstrated significantly improved clinical outcomes for management of depression, anxiety, and stress with mHealth apps vs control groups, with associated improvements in quality of life.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [19] Linardon, J., Cuijpers, P., Carlbring, P., Messer, M. & Fuller-Tyszkiewicz, M. The efficacy of app-supported smartphone interventions for mental health problems: a meta-analysis of randomized controlled trials. World Psychiatry 18, 325–336 (2019). source
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
Interpretationnone givenApps from each of these categories have the potential to hold value for patients when used as part of a clinical workflow; however, the level of evidence is currently only sufficient to support the use of apps in a small number of defined clinical scenarios.
“Apps from each of these categories have the potential to hold value for patients when used as part of a clinical workflow; however, the level of evidence is currently only sufficient to support the use of apps in a small number of defined clinical scenarios.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
There is little clinical guidance on how mHealth apps should be utilized to add value to patient care.
“Despite growing interest from both patients and healthcare providers, there is little clinical guidance on how mHealth apps should be utilized to add value to patient care”
The paper relies on this without support.
Patients are often not involved in the development of mobile health guidelines, and professionals’ views regarding high-quality health apps may not reflect patients’ views.
“Patients are often not involved in the development of mobile health guidelines, and professionals’ views regarding high-quality health apps may not reflect patients’ views.”
The paper relies on this without support.
Data security and privacy are a major concern for patients when using mHealth apps.
“We discuss data security and privacy as a major concern for patients when using mHealth apps.”
The paper relies on this without support.
More often than not mHealth developers do not have the resources available to fund large premarket prospective multicenter randomized controlled trials, and regulators currently do not insist on this.
“More often than not mHealth developers simply do not have the resources available to fund large premarket prospective multicenter randomized controlled trials (equivalent to Phase III studies in the development of a pharmaceutical) and regulators currently do not insist on this.”
The paper relies on this without support.
Many mHealth app developers are under pressure from early stage investors to demonstrate short-term product growth.
“Furthermore, many mHealth app developers are under pressure from early stage investors to demonstrate short-term product growth.”
The paper relies on this without support.
Preliminary data suggest there is no significant increase in adverse events with short-term use of CBT based digital therapeutics.
“Preliminary data suggest there is no significant increase in adverse events with short-term use of CBT based digital therapeutics.”
The paper relies on this without support.
Clinicians often lack the necessary training to counsel patients on risks that are unique to mHealth such as data privacy, and there is a lack of guidance on how to integrate digital technologies into established treatment protocols.
“Clinicians often lack the necessary training to counsel patients on risks that are unique to mHealth such as data privacy, which are very important to patients, and there is a lack of guidance on how to integrate digital technologies into established treatment protocols.”
The paper relies on this without support.
510k clearance requires the manufacturer to demonstrate substantial equivalence to a previously approved medical device but the standards of evidence for clinical effectiveness are variable and different to those that apply to pharmaceuticals.
“510k clearance requires the manufacturer to demonstrate substantial equivalence to a previously approved medical device but the standards of evidence for clinical effectiveness are variable and different to those that apply to pharmaceuticals.”
The paper relies on this without support.
There is no evidence based standardized approach to counselling patients on mHealth apps.
“Counselling patients on mHealth apps is challenging and there is no evidence based standardized approach.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Economic evidence of digital solutions in primary care is rare.
“Economic evidence of digital solutions in primary care is rare, indicating the significance of this paper to drive more research into the true economic benefit digital solutions can hold in general practice.”
From A prospective observational real world feasibility study assessing the role of app-based remote patient monitoring in reducing primary care clinician workload during the COVID pandemic (2021) · cites this paper as [26]
The ‘Challenges in taking an evidence-based approach to clinical use of mHealth apps’ section says high-quality clinical evidence is limited, while ‘What value will clinical use of mHealth hold for patients in the future?’ describes cost-effectiveness as a potential benefit. Neither establishes that economic evidence for digital solutions in primary care is rare. Searches for economic evidence, cost-effectiveness, and primary care found no passage making that point; page numbers were unavailable in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Metadata
- Authors
- Rowland, S. P., Fitzgerald, J. E., Holme, T., Powell, J., & McGregor, A.
- Affiliations
- Imperial College London (GB)
- KPMG (United Kingdom) (GB)
- St Helier Hospital (GB)
- University of Oxford (GB)
- Corresponding author
- Rowland, Simon P.
- Venue
- npj Digital Medicine
- Published
- 2020-01-13
- Volume
- 3
- Issue
- 1
- Pages
- 4-4
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC6957674
- OpenAlex
- W2999080334
- Semantic Scholar
- 9259b6a5fe5c3e74c893ee17b2168863cb33f706
- Cited by
- 454
- References
- 42
- Citations by year
- 2026: 34 · 2025: 82 · 2024: 92 · 2023: 94 · 2022: 75 · 2021: 62 · 2020: 13
- FWCI
- 11.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- Care Oxford at Oxford Health NHS Foundation Trust. National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Keywords
- mobile health, mobile health apps, clinical value, patient care, behavior change, digital self-management, FDA 510(k) clearance, data security, privacy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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