The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index; 2 of the 37 sources its claims cite were checked for support.
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
Registry
Cites
Statistics
Verification
Claims the paper makes
32 Not checked yet · 6 Own interpretation · 2 Supported · 2 No basis given · 1 Own result
Every source the paper cites for this claim was checked and supports it.
In the paper
“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.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Digital therapeutics
What the sources say
- Supportedcites [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
In “Summary of existing methods to improve app quality,” the paper says poor-quality apps still rise to the top of app-store lists. Its comparison of 47 smoking-cessation apps shows that store ranking is a poor guide to the quality of the evidence underlying an app. (LLM, conf=0.90)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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.”
§ What is the clinical value of mHealth for patients? > Challenges in taking an evidence-based approach to clinical use of mHealth apps
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The majority of the evidence included in this review is collected from clinical studies performed over short time periods.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > mHealth apps that deliver disease-related education
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“A systematic review noted that only 4 out of 805 medication adherence apps included had associated published evidence of clinical outcomes.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Behavior change interventions through mHealth apps
What the sources say
- Not checked yetcites [15] Ahmed, I. et al. Medication adherence apps: review and content analysis. JMIR mHealth uHealth. https://doi.org/10.2196/mhealth.6432 (2018). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“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.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Behavior change interventions through mHealth apps
What the sources say
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“a recent systematic review demonstrated that interactive symptom checkers are the most frequently investigated category of diagnostic app.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Diagnostics and clinical decision making through mHealth apps
What the sources say
- Not checked yetcites [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
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“A recent review identified 704 apps on the app store utilizing behavioral strategies to enhance medication adherence through alerts, reminders, and logs.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Behavior change interventions through mHealth apps
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“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)).”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Diagnostics and clinical decision making through mHealth apps
What the sources say
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“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).”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Diagnostics and clinical decision making through mHealth apps
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“However diagnostic accuracy was only 34% and triage advice was considered appropriate in only 55% of non-emergent cases.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Diagnostics and clinical decision making through mHealth apps
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 12
- Probably not a substantial claim. 4
Every source the paper cites for this claim was checked and supports it.
In the paper
“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.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Behavior change interventions through mHealth apps
What the sources say
- Supportedcites [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
Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ What is the clinical value of mHealth for patients? > Challenges in taking an evidence-based approach to clinical use of mHealth apps
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Behavior change interventions through mHealth apps
What the sources say
- Not checked yetcites [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
- Not checked yetcites [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
boostedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Diagnostics and clinical decision making through mHealth apps
What the sources say
- Not checked yetcites [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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Behavior change interventions through mHealth apps
What the sources say
- Not checked yetcites [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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Published evidence suggests that symptom checker apps are often risk averse and may lead to unnecessary consultations in the non-emergency setting.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Diagnostics and clinical decision making through mHealth apps
What the sources say
- Not checked yetcites [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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“However, experts have highlighted that current evidence is not at a level to suggest that apps screening photographs could replace a clinical consultation.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Diagnostics and clinical decision making through mHealth apps
What the sources say
- Not checked yetcites [6] The Lancet Oncology. Digital oncology apps: revolution or evolution? Lancet Oncol. 19, 999 (2018). source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“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%.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Behavior change interventions through mHealth apps
What the sources say
- Not checked yetcites [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
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > mHealth apps that deliver disease-related education
What the sources say
- Not checked yetcites [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
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ What is the clinical value of mHealth for patients? > Challenges in taking an evidence-based approach to clinical use of mHealth apps
What the sources say
- Not checked yetcites [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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“There are thousands of health-related apps available for download from the app store but only a tiny fraction are appropriate for clinical use.”
§ What is the clinical value of mHealth for patients? > How can clinicians support patients in obtaining value from mHealth apps?
What the sources say
- Not checked yetcites [15] Ahmed, I. et al. Medication adherence apps: review and content analysis. JMIR mHealth uHealth. https://doi.org/10.2196/mhealth.6432 (2018). source
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ What is the clinical value of mHealth for patients? > Challenges in taking an evidence-based approach to clinical use of mHealth apps
What the sources say
- Not checked yetcites [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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Digital therapeutics
What the sources say
- Not checked yetcites [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
plaincorrelationaln = 1,794- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“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.”
§ What is the clinical value of mHealth for patients? > The scope of mHealth functionalities that have shown clinical evidence for patient care > Digital therapeutics
What the sources say
- Not checked yetcites [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
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
§ What is the clinical value of mHealth for patients? > Conclusions
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
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?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measured
Citations
Cites
Perski, O., Blandford, A., West, R., & Michie, S. (2017) · Translational Behavioral Medicine · doi:10.1007/s13142-016-0453-1 · Cited by 1,193
Linardon, J., Cuijpers, P., Carlbring, P., Messer, M., & Fuller‐Tyszkiewicz, M. (2019) · World Psychiatry · doi:10.1002/wps.20673 · Cited by 860
Flores Mateo, G., Granado-Font, E., Ferré-Grau, C., & Montaña-Carreras, X. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4836 · Cited by 540
Evaluation of symptom checkers for self diagnosis and triage: audit study
Semigran, H. L., Linder, J. A., Gidengil, C., & Mehrotra, A. (2015) · BMJ · doi:10.1136/bmj.h3480 · Cited by 489
Denis, F., Lethrosne, C., Pourel, N., Molinier, O., Pointreau, Y., Domont, J., Bourgeois, H., Senellart, H., Trémolières, P., Lizée, T., et al. (2017) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/djx029 · Cited by 482
Rowe, A. K., Rowe, S. Y., Peters, D. H., Holloway, K. A., Chalker, J., & Ross-Degnan, D. (2018) · The Lancet Global Health · doi:10.1016/s2214-109x(18)30398-x · Cited by 482
Ryu, S. (2012) · Healthcare Informatics Research · doi:10.4258/hir.2012.18.3.231 · Cited by 451
Morawski, K., Ghazinouri, R., Krumme, A., Lauffenburger, J. C., Lu, Z., Durfee, E., Oley, L., Lee, J., Mohta, N., Haff, N., et al. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.0447 · Cited by 333
Prescribable mHealth apps identified from an overview of systematic reviews
Byambasuren, O., Sanders, S., Beller, E., & Glasziou, P. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0021-9 · Cited by 291
Wu, Y., Yao, X., Vespasiani, G., Nicolucci, A., Dong, Y., Kwong, J., Li, L., Sun, X., Tian, H., & Li, S. (2017) · JMIR mHealth and uHealth · doi:10.2196/mhealth.6522 · Cited by 283
Cited by
Sorted by citations
Items 101–200 of 454
A Survey of Notification Designs in Commercial mHealth Apps
Woodward, J., Chen, Y.-P., Jurczyk, K., Ross, K. M., Anthony, L., & Ruiz, J. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451639 · Cited by 19
mHealth and its application in menstrual related issues: a systematic review
Kalampalikis, A., Chatziioannou, S. S., Πρωτόπαπας, Α., Gerakini, A., & Michala, L. (2021) · The European Journal of Contraception & Reproductive Health Care · doi:10.1080/13625187.2021.1980873 · Cited by 19
Wearables, the Marketplace and Efficiency in Healthcare: How Will I Know That You’re Thinking of Me?
Howard, M. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00473-4 · Cited by 18
Wohlgemut, J. M., Pisirir, E., Kyrimi, E., Stoner, R. S., Marsh, W., Perkins, Z., & Tai, N. R. M. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad051 · Cited by 19
Schwartz, D. G., Spitzer, S., Khalemsky, M., Bejar, A. H. C., Ray, S., Chiou, J., Sakhnini, R., Lanin, R., Meir, M. M., & Tsai, M. (2024) · Health Policy and Technology · doi:10.1016/j.hlpt.2024.100848 · Cited by 18
Mobile health application for Thai women: investigation and model
Kongjit, C., Nimmolrat, A., & Khamaksorn, A. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01944-0 · Cited by 17
Chandler, R. D., Guillaume, D., Parker, A. G., Carter, S. E., & Hernandez, N. (2020) · Perspectives on Sexual and Reproductive Health · doi:10.1363/psrh.12170 · Cited by 19
König, L., & Suhr, R. (2023) · Journal of Medical Internet Research · doi:10.2196/46336 · Cited by 16
Tonga, E., Williamson, E. M., Srikesavan, C. S., Özen, T., Sarıtaş, F., & Lamb, S. E. (2021) · Rheumatology International · doi:10.1007/s00296-021-04860-0 · Cited by 16
Denis, F., & Krakowski, I. (2021) · Journal of Medical Internet Research · doi:10.2196/30549 · Cited by 16
A Blockchain-Based Regulatory Framework for mHealth
Marbouh, D., Simsekler, M. C. E., Salah, K., Jayaraman, R., & Ellahham, S. H. (2022) · Data · doi:10.3390/data7120177 · Cited by 15
Mitchaϊ, P. M., Mapinduzi, J., Verbrugghe, J., Michiels, S., Janssens, L., Kossi, O., Bonnechère, B., & Timmermans, A. A. A. (2023) · European Spine Journal · doi:10.1007/s00586-023-07964-2 · Cited by 15
Hernandez, N., Davis, M. V., Farinu, O., Hernandez-Spalding, K., Lewis, K., Beshara, M. S., Francis, S., Baker, L. J., Byrd, S., Parker, A. G., et al. (2024) · Women s Health · doi:10.1177/17455057241239769 · Cited by 15
Jain, Y. S., Garg, A., Jhamb, D. K., Jain, P., & Karar, A. (2019) · Cardiovascular & Hematological Agents in Medicinal Chemistry · doi:10.2174/1871525717666190912152938 · Cited by 16
Davoudi, M., Ghezeljeh, T. N., & Aghouee, F. V. (2020) · JMIR Nursing · doi:10.2196/20747 · Cited by 15
Heuristic Evaluation of a Top-Rated Diabetes Self-Management App
Harrington, L., Parker, C. D., Ulanday, K., & Harrington, C. (2021) · Applied Clinical Informatics · doi:10.1055/s-0041-1736628 · Cited by 15
Person-Generated Health Data in Women’s Health: Protocol for a Scoping Review
Karim, J. L., & Talhouk, A. (2021) · JMIR Research Protocols · doi:10.2196/26110 · Cited by 14
Tudor, A. I. M., Nichifor, E., Litră, A. V., Chițu, I. B., Bratucu, T.-O., & Brătucu, G. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19159172 · Cited by 14
Kouri, A., & Gupta, S. (2023) · CHEST Pulmonary · doi:10.1016/j.chpulm.2023.100002 · Cited by 14
Venkatraman, V., Kirsch, E. P., Luo, E., Kunte, S., Ponder, M., Gellad, Z. F., Liu, B., Lee, H.-J., Jung, S.-H., Haglund, M. M., et al. (2022) · JMIR Perioperative Medicine · doi:10.2196/38690 · Cited by 13
Gvozdanovic, A., Jozsa, F., Fersht, N. L., Grover, P. J., Kirby, G., Kitchen, N. D., Mangiapelo, R., McEvoy, A. W., Miserocchi, A., Patel, R., et al. (2022) · BMJ Surgery Interventions & Health Technologies · doi:10.1136/bmjsit-2021-000130 · Cited by 13
Mpox-AISM: AI-mediated super monitoring for mpox and like-mpox
Yue, Y., Li, Z., Zhang, X., Wang, Z., Zhang, X., Zhang, F., Saravanakumar, S., Xu, J., Zhao, W., Liu, J., et al. (2024) · iScience · doi:10.1016/j.isci.2024.109766 · Cited by 14
Nurses’ Use of mHealth Apps for Chronic Conditions: Cross-Sectional Survey
Shiyab, W., Rolls, K., Ferguson, C., & Halcomb, E. (2024) · JMIR Nursing · doi:10.2196/57668 · Cited by 15
Herold, M., Simbula, S., & Gallucci, M. (2024) · Current Psychology · doi:10.1007/s12144-024-06534-z · Cited by 13
Mahadik, S., Sen, P., & Shah, E. J. (2025) · Digital Health · doi:10.1177/20552076251362097 · Cited by 15
Evaluation of Mobile Health apps for Non-Medical Cannabis Use: A Scoping Review
Sedrati, H., Belrhiti, Z., Nejjari, C., & Ghazal, H. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2021.12.052 · Cited by 13
Emerson, M. R., Dinkel, D. M., Watanabe‐Galloway, S., Torous, J. B., & Johnson, D. J. (2023) · Translational Behavioral Medicine · doi:10.1093/tbm/ibad016 · Cited by 12
Old age or cognitive decline? Examining the usability of a mobile health app for older Australians
McCarthy, B., Sabharwal, J. K., & Chawla, S. (2024) · Informatics for Health and Social Care · doi:10.1080/17538157.2024.2332691 · Cited by 12
Jiang, J., & Zheng, Z. (2024) · JMIR mhealth and uhealth · doi:10.2196/55061 · Cited by 12
Gotlieb, E. G., Marzoughi, S., Kwon, C., Harmon, M., Kimura, M., Truesdale, A., Sweetnam, C., Soudant, C., Downes, M., Busis, N. A., et al. (2025) · Epilepsia · doi:10.1111/epi.18288 · Cited by 12
Real-World Mobile Health Implementation and Patient Safety: Multicenter Qualitative Study
Su, J. J., Chan, M. H. S., Ghisi, G. L. D. M., Kwan, R. Y. C., Wong, A. K. C., Lin, R. S. Y., Yeung, W., He, Q., Pepera, G., & Baťalík, L. (2025) · Journal of Medical Internet Research · doi:10.2196/71086 · Cited by 18
Directly observed therapy to promote medication adherence in adolescent heart transplant recipients
Killian, M., Clifford, S. A., Lustria, M. L. A., Skivington, G. L., & Gupta, D. (2022) · Pediatric Transplantation · doi:10.1111/petr.14288 · Cited by 12
Promoting Health Education through Mobile Apps: A Quantitative Analysis of American Hospitals
Aguerrebere, P. M., Medina, E., & Pacanowski, T. G. (2022) · Healthcare · doi:10.3390/healthcare10112231 · Cited by 12
mHealth Interventions for Contraceptive Behavior Change in the United States: A Systematic Review
Cartwright, A. F., Alspaugh, A., Britton, L. E., & Noar, S. M. (2022) · Journal of Health Communication · doi:10.1080/10810730.2022.2044413 · Cited by 15
Charumbira, M. Y., Conradie, T., Berner, K., & Louw, Q. A. (2024) · BMC Health Services Research · doi:10.1186/s12913-024-10564-5 · Cited by 11
Dauletbaev, N., Oftring, Z. S., Akik, W., Michaelis-Braun, L., Korel, J., Lands, L. C., Waldmann, S., Müller, B. S., Dreher, M., Rohde, G. G. U., et al. (2022) · Paediatric Respiratory Reviews · doi:10.1016/j.prrv.2022.01.002 · Cited by 11
Rosa, I., Lages, M., Grilo, C., Barros, R., & Guarino, M. P. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.862065 · Cited by 12
Hyzy, M., Bond, R., Mulvenna, M. D., Bai, L., Dix, A. J., Daly, R., Frey, A.-L., & Leigh, S. (2023) · JMIR mhealth and uhealth · doi:10.2196/47043 · Cited by 11
Karimi, N., Opie, R., Crawford, D. A., O’Connell, S. L., Hamblin, P. S., Steele, C. E., & Ball, K. (2022) · JMIR Formative Research · doi:10.2196/37429 · Cited by 10
Jenciūtė, G., Kasputytė, G., Bunevičienė, I., Korobeinikova, E., Vaitiekus, D., Inčiūra, A., Jaruševičius, L., Bunevičius, R., Krikštolaitis, R., Krilavičius, T., et al. (2023) · JMIR Research Protocols · doi:10.2196/49096 · Cited by 12
Wearable rehabilitation wristband for distal radius fractures
Zha, Q., Xu, Z., Cai, X., Zhang, G., & Shen, X. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1238176 · Cited by 10
López‐Alcalde, J., Wieland, L. S., Barth, J., Grainger, R., Baxter, N. N., Heron, N., Triantafyllidis, A. K., Carrión, C., Trecca, E. M. C., Holl, F., et al. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105345 · Cited by 10
Nelson, E. C., Verhagen, T., Vollenbroek-Hutten, M. M. R., & Noordzij, M. L. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108314 · Cited by 10
Alshammari, H. M., AlMutairi, R. I., Ghazwani, A. Y., & Aditya, R. S. (2025) · Digital Health · doi:10.1177/20552076251346698 · Cited by 10
Sun, Y., Li, Y., He, F. J., Liu, H., Sun, J., Luo, R., Guo, C., & Zhang, P. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.744881 · Cited by 10
IT-based regulation of personal health: Nudging, mobile apps and data
Davidson, E., Winter, J. S., & Chiasson, M. W. (2022) · Journal of Information Technology · doi:10.1177/02683962221112678 · Cited by 12
A Novel Bayesian General Medical Diagnostic Assistant Achieves Superior Accuracy With Sparse History
Jones, A. M., & Jones, D. R. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.727486 · Cited by 10
Digital Innovation in Healthcare Entrepreneurship
Mohammed-Nasir, R., Oshikoya, K. A., & Oreagba, I. A. (2023) · Springer eBooks · doi:10.1007/978-981-19-6696-5_22 · Cited by 13
Jibb, L., Liu, W., Stinson, J., Nathan, P. C., Chartrand, J., Alberts, N. M., Hashemi, E., Masama, T., Pease, H. G., Torres, L. B., et al. (2023) · Paediatric and Neonatal Pain · doi:10.1002/pne2.12097 · Cited by 10
Smartphones and Learning: Evaluating the Focus of Recent Research
Hartley, K., Hoffman, B. H., & Andújar, A. (2023) · European Journal of Investigation in Health Psychology and Education · doi:10.3390/ejihpe13040056 · Cited by 13
A Transgender Health Information Resource: Participatory Design Study
Morse, B., Soares, A., Kwan, B. M., Allen, M., Lee, R. S., DeSanto, K., Holliman, B. D., Ytell, K., & Schilling, L. M. (2023) · JMIR Human Factors · doi:10.2196/42382 · Cited by 10
The Promise and Risks of mHealth in Heart Failure Care
Haywood, H. B., Sauer, A. J., Allen, L. A., Albert, N. M., & DeVore, A. D. (2023) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2023.07.005 · Cited by 10
Mobile Health Interventions: A Frontier for Mitigating the Global Burden of Cardiovascular Disease
Liaqat, M., Mushtaq, M., Jamil, A., Mushtaq, M. M., Ali, H., Anwar, R., Raza, A., Aslam, A., Tariq, T., Hussain, M., et al. (2024) · Cureus · doi:10.7759/cureus.62157 · Cited by 10
Allgaier, J., Schlee, W., Langguth, B., Probst, T., & Pryss, R. C. (2021) · Scientific Reports · doi:10.1038/s41598-021-96731-8 · Cited by 10
Evaluation of the Wateen App in the Blood-Donation Process in Saudi Arabia
Alessa, T. (2022) · Journal of Blood Medicine · doi:10.2147/jbm.s360091 · Cited by 10
Prediction of Tinnitus Perception Based on Daily Life MHealth Data Using Country Origin and Season
Allgaier, J., Schlee, W., Probst, T., & Pryss, R. C. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11154270 · Cited by 10
Reconstrucción del sistema sanitario: gobernanza, organización y digitalización. Informe SESPAS 2022
López‐Valcárcel, B. G., & Rubio, V. O. (2022) · Gaceta Sanitaria · doi:10.1016/j.gaceta.2022.02.010 · Cited by 10
Mobile apps for cancer patients: Identifying positive impacts and concerns
Lei-dong, W., Monachino, M. S., Lloyd-Williams, D., Nguyen, T. L. H., D’Souza, B., & Moreira, P. (2025) · Digital Health · doi:10.1177/20552076241305707 · Cited by 10
Ethical issues in m-Health applications in community health work in India — a scoping review
Gopichandran, V., Subramaniam, S., & Thiagesan, R. (2023) · Indian Journal of Medical Ethics · doi:10.20529/ijme.2023.037 · Cited by 9
Milne‐Ives, M., Rahman, E., Bradwell, H. L., Baines, R., Boey, T., Potter, A., Lawrence, W. T., van Velthoven, M. H., & Meinert, E. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000481 · Cited by 10
Reid, K. M., Beswick, E., Tam, J., Symonds, A., Lyle, D., Newton, J., Perry, D., Neale, J., Chandran, S., & Pal, S. (2024) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2024.123072 · Cited by 10
Flores, J. S., Dalal, A. K., Sousa, J., Foer, D., Rodriguez, J. A., Plombon, S., Bates, D. W., Arcia, A., & Rudin, R. S. (2024) · Applied Clinical Informatics · doi:10.1055/s-0044-1788978 · Cited by 10
Salajegheh, M., Rooholamini, A., & Norouzi, A. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05253-0 · Cited by 8
Patients Can Administer Mobile Audio Recordings to Increase Knowledge in Advanced Prostate Cancer
Kwon, D. H.-M., Trihy, L., Darvish, N., Hearst, E. L., Sumra, S., Borno, H. T., Bose, R., Chou, J., de Kouchkovsky, I., Desai, A., et al. (2024) · Cancer Medicine · doi:10.1002/cam4.70433 · Cited by 8
Frey, A.-L., Phillips, B., Baines, R., McCabe, A., Elmes, E., Yeardsley-Pierce, E., Wall, R., Parry, J., Vose, A., Hewitt, J., et al. (2024) · Health and Technology · doi:10.1007/s12553-024-00934-8 · Cited by 9
López‐Alcalde, J., Wieland, L. S., Yan, Y., Barth, J., Khami, M. R., Shivalli, S., Lokker, C., Kaur, H., Macharia, P. M., Yun, S., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/53187 · Cited by 10
Les applications numériques en santé mentale : état des lieux, enjeux et perspectives
Mouchabac, S., Bourla, A., Geoffroy, P. A., Micoulaud‐Franchi, J., Misdrahi, D., Petauton, D., Samalin, L., Conejero, I., Schwan, R., & Bonnot, O. (2023) · Annales Médico-psychologiques revue psychiatrique · doi:10.1016/j.amp.2022.12.015 · Cited by 8
Pinto, A., Martinho, D., Matos, J., Greer, D., Vieira, A., Ramalho, A. L. C., Marreiros, G., & Freitas, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120726 · Cited by 8
Best Practices for Developing a Diabetes mHealth App
Holtz, B., & Mitchell, K. M. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231195849 · Cited by 8
Franco‐Arellano, B., Brown, J. M., Daggett, Q., Lockhart, C., Kapralos, B., LeSage, A., & Arcand, J. (2023) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2023-0214 · Cited by 9
Informing Patient-Provider Engagement for Shared Decision Making Through Mobile Health Applications
Shih, H.-J., Xue, H., Min, H., Wojtusiak, J., & Chang, J. (2023) · Patient Preference and Adherence · doi:10.2147/ppa.s438710 · Cited by 8
Czuber, N. K., Garabedian, P. M., Rice, H. E., Tejeda, C. J., Dykes, P. C., & Latham, N. K. (2024) · Applied Clinical Informatics · doi:10.1055/a-2267-1727 · Cited by 8
Hypnosis Apps: A Systematic Review
Scheffrahn, K., Alldredge, C. T., Snyder, M., & Elkins, G. R. (2024) · International Journal of Clinical and Experimental Hypnosis · doi:10.1080/00207144.2024.2434085 · Cited by 8
Alipour, J., Mehdipour, Y., Zakerabasali, S., & Karimi, A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0316631 · Cited by 8
Ivanitskaya, L. V., Zikos, D., & Erzikova, E. V. (2025) · Journal of Medical Internet Research · doi:10.2196/60071 · Cited by 11
Quantity and type of peer-reviewed evidence for popular free medical apps: Cross-sectional review
Wong, D., Nwe, K., Evans, R., Nelissen, N., & Larsen, M. E. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104416 · Cited by 8
Perceptions of Security and Privacy in mHealth
Ferreira, A., Muchagata, J., Vieira‐Marques, P., Abrantes, D., & Teles, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77392-2_19 · Cited by 8
Schwab, J. D., Schobel, J., Werle, S. D., Fürstberger, A., Ikonomi, N., Szekely, R., Thiam, P., Hühne, R., Jahn, N., Schuler, R., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3087315 · Cited by 8
Faqar-Uz-Zaman, S. F., Sliwinski, S., Mohr, L., Dreilich, J., Filmann, N., Detemble, C., Zmuc, D., Chun, F. K. ‐., Derwich, W., Schreiner, W., et al. (2024) · JMIR mhealth and uhealth · doi:10.2196/55298 · Cited by 7
Stubberud, A., Linde, M., & Tronvik, E. (2025) · Cephalalgia · doi:10.1177/03331024251348651 · Cited by 8
Kheirinejad, S., Alorwu, A., Visuri, A., & Hosio, S. J. (2022) · JMIR Human Factors · doi:10.2196/38265 · Cited by 7
Ting, A., & McLachlan, C. (2023) · PeerJ · doi:10.7717/peerj.15366 · Cited by 6
Liu, V., Kaila, M., & Koskela, T. (2024) · JMIR Human Factors · doi:10.2196/55099 · Cited by 7
Hanson, O. R., Khan, I. I., Khan, Z. H., Amin, M. A., Biswas, D., Islam, M. T., Nelson, E. J., Ahmed, S. M., Brintz, B. J., Hegde, S. T., et al. (2024) · Journal of Global Health · doi:10.7189/jogh.14.04185 · Cited by 7
Lee, S. R., Kim, J., & Kim, H. (2024) · Journal of Medical Internet Research · doi:10.2196/63166 · Cited by 8
Tate, A. R., & Rao, G. H. R. (2020) · International Journal of Biomedicine · doi:10.21103/article10(3)_ra2 · Cited by 7
Integration of e-Health Strategies for Post-COVID-19 Pandemic Pediatric Weight Management Programs
Yudkin, J. S., Messiah, S. E., Allicock, M. A., & Barlow, S. E. (2023) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2023.0068 · Cited by 6
Balderas-Díaz, S., Rodríguez‐Fórtiz, M. J., Garrido, J. L. G., Bellido‐González, M., & Guerrero-Contreras, G. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-02036-9 · Cited by 14
Health professionals’ experiences with a patient portal pre and post launch: A qualitative study
Janssen, A. L., Keep, M., Selvadurai, H. C., & Shaw, T. J. (2023) · Health Policy and Technology · doi:10.1016/j.hlpt.2023.100761 · Cited by 6
Analyzing the Role of Health Apps in Promoting Healthy Lifestyle and Preventive Care
Kotha, H. K. (2023) · International Journal of Science and Research (IJSR) · doi:10.21275/sr231111061822 · Cited by 6
Ezeigwe, O. J., Nwosu, K. O. S., Afolayan, O. K., Ojaruega, A. A., Echere, J., Desai, M., Onigbogi, M. O., Oladoyin, O. O., Okoye, N. C., & Fwelo, P. (2024) · Journal of Medical Internet Research · doi:10.2196/63403 · Cited by 6
Mobile Health Study Incorporating Novel Fitness Test
Johnson, A., Hershman, S. G., Javed, A., Mattsson, C. M., Christle, J. W., Oppezzo, M., & Ashley, E. A. (2022) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-022-10317-x · Cited by 6
ADHERENCE TO TREATMENT – REVIEWING METHODS OF ASSESSMENT AND IMPROVEMENT
Rusu, R.-N. (2023) · FARMACIA · doi:10.31925/farmacia.2023.2.2 · Cited by 7
Clinical application of physical therapy in facial paralysis treatment: A review
DeBord, K., Ding, P., Harrington, M. G., Duggal, R., Genther, D. J., Ciolek, P. J., & Byrne, P. J. (2023) · Journal of Plastic Reconstructive & Aesthetic Surgery · doi:10.1016/j.bjps.2023.10.076 · Cited by 7
Gómez, M. M., Julián, E., Armengou, L., & Pividori, M. I. (2024) · Talanta · doi:10.1016/j.talanta.2024.126106 · Cited by 6
Chatzimina, M., Papadaki, H. Α., Pontikoglou, C. G., Koumakis, L., Kostas Marias, A., & Tsiknakis, M. (2021) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi50953.2021.9508587 · Cited by 6
Pulmonary rehabilitation: one size does not fit all
Cox, N. S., & Holland, A. E. (2025) · Thorax · doi:10.1136/thorax-2024-222680 · Cited by 6
Wright, L. A., Majid, M., Shajan, G., Momoh, G., Patil, R., Rawsthorne, M., Purewal, D., Patel, S., & Morriss, R. K. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02118-8 · Cited by 5
Oh, K. M., Hong, S.-S., Beran, K., Song, Y., & Lee, J. (2025) · Sage Open Aging · doi:10.1177/30495334251398089 · Cited by 5
Kochashvili, M., Guttoo, P., Sezgın, E., Pai, A. L. H., Bajwa, R. P. S., Landier, W., Gerhardt, C. A., & Skeens, M. A. (2025) · JMIR Cancer · doi:10.2196/66847 · Cited by 5
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).
Cited sources are checked with a language model’s help (gpt-6-sol).
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
