Cited by 331Mobile Health and mHealth ApplicationsDigital Mental Health InterventionsTelemedicine and Telehealth Implementation

The DOI matches its Crossref record; 83 of the 84 references it cites were found in a registry or an index; none of the 34 sources its claims cite is checked for support yet.

Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence

Open access

Sieverink, Floor, Kelders, Saskia M, van Gemert-Pijnen, Julia EWC·2017·Journal of Medical Internet Research

DOI 10.2196/jmir.8578PMC PMC5738543

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Abstract

BACKGROUND: In electronic health (eHealth) evaluations, there is increasing attention for studying the actual usage of a technology in relation to the outcomes found, often by studying the adherence to the technology. On the basis of the definition of adherence, we suggest that the following three elements are necessary to determine adherence to eHealth technology: (1) the ability to measure the usage behavior of individuals; (2) an operationalization of intended use; and (3) an empirical, theoretical, or rational justification of the intended use. However, to date, little is known on how to operationalize the intended usage of and the adherence to different types of eHealth technology. OBJECTIVE: The study aimed to improve eHealth evaluations by gaining insight into when, how, and by whom the concept of adherence has been used in previous eHealth evaluations and finding a concise way to operationalize adherence to and intended use of different eHealth technologies. METHODS: A systematic review of eHealth evaluations was conducted to gain insight into how the use of the technology was measured, how adherence to different types of technologies was operationalized, and if and how the intended use of the technology was justified. Differences in variables between the use of the technology and the operationalization of adherence were calculated using a chi-square test of independence. RESULTS: In total, 62 studies were included in this review. In 34 studies, adherence was operationalized as "the more use, the better," whereas 28 studies described a threshold for intended use of the technology as well. Out of these 28, only 6 reported a justification for the intended use. The proportion of evaluations of mental health technologies reporting a justified operationalization of intended use is lagging behind compared with evaluations of lifestyle and chronic care technologies. The results indicated that a justification of intended use does not require extra measurements to determine adherence to the technology. CONCLUSIONS: The results of this review showed that to date, justifications for intended use are often missing in evaluations of adherence. Evidently, it is not always possible to estimate the intended use of a technology. However, such measures do not meet the definition of adherence and should therefore be referred to as the actual usage of the technology. Therefore, it can be concluded that adherence to eHealth technology is an underdeveloped and often improperly used concept in the existing body of literature. When defining the intended use of a technology and selecting valid measures for adherence, the goal or the assumed working mechanisms should be leading. Adherence can then be standardized, which will improve the comparison of adherence rates to different technologies with the same goal and will provide insight into how adherence to different elements contributed to the outcomes.

AI snapshot

Written from the paper’s full text.

TL;DR

This systematic review of 62 eHealth evaluations finds that adherence is often operationalized as “the more use, the better” without an intended-use threshold, and even when a threshold is given, justification is usually missing. The authors argue adherence should be defined against a justified intended use tied to the technology’s goal or assumed working mechanisms.

Key findings

Of 62 included studies, 34 used Category A (“more use, the better”), 23 provided an intended-use threshold without justification (Category B), and 5 provided a justified threshold (Category C); the abstract reports 6 justified among 28 threshold studies.
Mental health technologies lagged: 20/31 had Category A operationalizations, while Category C was more equally distributed across mental health, lifestyle, and chronic care.
Most operationalizations used ≤2 measures (49/62); 10/13 with ≥3 measures were Category A. Adherence to unstructured technologies more often used logins/sessions (P=.03), features accessed/used (P<.001), and time spent (P<.001); structured technologies used modules/lessons completed (P<.001).
Technologies were mostly Web-based (51/62); 29 were structured, 18 unstructured, and 15 hybrid, with half targeting mental health.

Methods

Systematic review; searched Scopus, Web of Science, ScienceDirect, and PsycINFO; 7005 records identified, 62 full texts included after title, abstract, and full-text screening.
Included health-related technologies intended for repeated use, with objective quantifiable usage measures and an adherence operationalization; excluded offline treatment adherence, protocol adherence, simple information-exchange tools, conference abstracts, and unavailable full texts.
Data were extracted and adherence operationalizations categorized as A/B/C; differences were tested with chi-square test of independence, using Monte Carlo correction when expected counts were low, at alpha .05.

Limitations

Stated: only studies using adherence as an outcome or explicitly stating a proxy were included, so studies using related terms (nonusage attrition, engagement, drop-out, [non]usage) may have been missed.
Stated: adherence operationalizations were categorized per study, not per technology, though the same technology could have different operationalizations across studies.
Evident from the text: the abstract and results differ on the number of justified operationalizations (6 vs 5), and the review is descriptive/categorical without meta-analytic synthesis.

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Integrity signals

Registry

Crossref · 10.2196/jmir.8578

Cites

83 of 83 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 34 cited sources checked · 0 supported · 0 flagged

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
“In 34 studies, adherence was operationalized as “the more use, the better,””
- Also stated in: § Operationalization of the Adherence Definition · plain
  “Out of the 62 included articles, 34 reported adherence only in terms of how often the technology was used (Category A operationalization)”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Abstract > Results
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
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
“However, to date, little is known on how to operationalize the intended usage of and the adherence to different types of eHealth technology.”
- Also stated in: § Determining Adherence · plain
  “However, to date, little is known about how to operationalize the intended usage of and thus the adherence to different types of eHealth technology.”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Abstract > Background
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes

Show all 57 claims

Probably not a substantial claim. 28
It may state two separate claims as one. 20
The quote only partly states this claim. 2

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
“In total, 62 studies were included in this review.”
- Also stated in: § Study Selection · plain
  “After screening of the titles, abstracts, and full texts, 62 full texts were included in this review.”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Abstract > Results
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Carolan et al describe a protocol for a study to be conducted to understand the optimum adherence to the technology in relation to the outcomes [78].”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Results > Operationalization of the Adherence Definition
What the sources say
- Not checked yetcites [78] source
Strength:plain· not yet calibrated
Tests✗ Asserted: fails✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it.
In the paper
“Beatty et al considered a user to be highly adherent when a therapeutic dose of 66% of the intervention was received [76]. This threshold for a therapeutic dose was obtained from previous studies.”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Results > Operationalization of the Adherence Definition
What the sources say
- Not checked yetcites [76] source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“In the study by Mertens et al, technology use was represented in relation to medication use, and users were therefore seen as adherent when the technology was used in accordance with the recommendations for medication use [77].”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Results > Operationalization of the Adherence Definition
What the sources say
- Not checked yetcites [77] Mertens, A, 2017, Advances in Human Factors and Ergonomics in Healthcare source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In the study by Zeng et al, the technology consisted of different elements that were all evaluated as effective in other studies [75]. Users were considered adherent if they used all elements.”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Results > Operationalization of the Adherence Definition
What the sources say
- Not checked yetcites [75] source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it. 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
“Therefore, it can be concluded that adherence to eHealth technology is an underdeveloped and often improperly used concept in the existing body of literature.”
- Also stated in: § Conclusions · plain
  “it can be concluded that adherence to eHealth technology is an underdeveloped and often improperly used concept in the existing body of literature.”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Abstract > Conclusions
Load-bearingStrength:boosted· not yet calibrated
Tests✓ Asserted: passes? Checkable: unclear✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
In the paper
“This is unexpected because the majority of mental health technologies is based on (principles of) CBT, which is the most studied treatment for depression and has proven effective in many studies [79].”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Discussion > Principal Findings
What the sources say
- Not checked yetcites [79] Cuijpers, P, 2013, Can J Psychiatry source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“On the basis of the definition of adherence, we suggest that the following three elements are necessary to determine adherence to eHealth technology: (1) the ability to measure the usage behavior of individuals; (2) an operationalization of intended use; and (3) an empirical, theoretical, or rational justification of the intended use.”
- Also stated in: § Determining Adherence · hedged
  “To summarize, based on the definition of adherence, we suggest that the following three elements are necessary to determine adherence to eHealth technology: (1) the ability to measure the usage behavior of individuals; (2) an operationalization of intended use; and (3) an empirical, theoretical, or rational justification of the intended use.”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Abstract > Background
What the sources say
- Not checked yetcites [10] Sabaté, E, 2003, Adherence to long-term therapies: evidence for action
Load-bearingStrength:hedged· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“An important reason for the lack of justifications for the intended use of eHealth technologies might be that there is a lack of knowledge regarding the working mechanisms of technology-based applications [8].”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Discussion > Implications and Recommendations
What the sources say
- Not checked yetcites [8] source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Moreover, definitions of engagement usually incorporate the more subjective attributes of challenge, positive affect, endurability, and aesthetic and sensory appeals [12], whereas adherence is mostly based on measures for usage behavior.”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Introduction > Adherence to eHealth Technologies
What the sources say
- Not checked yetcites [12] SmithWPlodererBWadleyGWebberSBorlandRTrajectories of Engagement and Disengagement with a Story-Based Smoking Cessation App20172017 CHI Conference on Human Factors in Computing SystemsMay 6-11, 2017Denver, ColoradoACM304556
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“However, a technology can be designed for multiple target groups and, depending on the individual user goals and the desired outcomes, technology can be used in many different ways in terms of the features that are used, as well as the frequency, time, and place of use [13,14].”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Introduction > Determining Adherence
What the sources say
- Not checked yetcites [13] source
- Not checked yetcites [14] source
Load-bearingStrength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
In the paper
“Kaushal and Rhodes found that exercising for at least four times per week for 6 weeks is a minimal requirement for establishing exercise habits [81].”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Discussion > Implications and Recommendations
What the sources say
- Not checked yetcites [81] source
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“the intended use or the “therapeutic dose” can be justified just as well using existing guidelines for healthy living and medication use [74,77] or using previous research regarding other technologies [75,76].”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Discussion > Implications and Recommendations
What the sources say
- Not checked yetcites [74] source
- Not checked yetcites [77] Mertens, A, 2017, Advances in Human Factors and Ergonomics in Healthcare source
- Not checked yetcites [75] source
- Not checked yetcites [76] source
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“In turn, the percentage of people who adhered to the intervention can be calculated, making adherence a more objective and standardized concept [1].”
§ Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence > Discussion > Implications and Recommendations
What the sources say
- Not checked yetcites [1] source
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.

40 of 40

Claims other papers make about this one

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Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

Abstract reports six justified studies, but results report five
Minor
The abstract says 6 studies justified their intended-use threshold, whereas the results identify 5 Category C studies. This changes the reported number meeting the paper's justification criterion.
Category A and B denominator is one too low
Minor
There are 34 Category A and 23 Category B operationalizations, totaling 57 rather than the stated 56.
Reported 56 · recomputed 57
Chronic-care percentage in Category C is incorrect
Minor
Table 4 gives one chronic-care study among five Category C studies, which is 20%, not 40%.
Reported 40 · recomputed 20
Claim of no measure differences conflicts with Table 4
Minor
The discussion says no significant differences were found between the kinds of measures used across the three categories, but Table 4 marks the days/weeks/months measure as significant (aP<.05).
Time-spent percentage in Category C is incorrect
Minor
Table 4 gives one study using time spent among five Category C studies, which is 20%, not 17%.
Reported 17 · recomputed 20
Smartphone-app count differs between prose and Table 3
Minor
The prose reports five smartphone apps, but Table 3 reports six, distributed as 3 structured, 2 unstructured, and 1 hybrid.

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Development and initial evaluation of blended cognitive behavioural treatment for major depression in routine specialized mental health care
Kooistra, L. C., Ruwaard, J., Wiersma, J. E., van Oppen, P., van der Vaart, R., van Gemert-Pijnen, J. E. W. C., & Riper, H. (2016) · Internet Interventions · doi:10.1016/j.invent.2016.01.003 · Cited by 92
Participants, Usage, and Use Patterns of a Web-Based Intervention for the Prevention of Depression Within a Randomized Controlled Trial
Kelders, S. M., Bohlmeijer, E. T., & Van Gemert-Pijnen, J. E. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2258 · Cited by 88
Self-citation
Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data
Sieverink, F., Kelders, S., Poel, M., & van Gemert-Pijnen, L. (2017) · JMIR Research Protocols · doi:10.2196/resprot.6452 · Cited by 88
Self-citation
Attrition in web-based treatment for problem drinkers
Postel, M. G., de Haan, H. A., ter Huurne, E. D., van der Palen, J., Becker, E. S., & de Jong, C. A. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1811 · Cited by 87
Weekly brief phone support in self-help cognitive behavioral therapy for insomnia disorder: Relevance to adherence and efficacy
Ho, F. Y., Chung, K., Yeung, W., Ng, T. H., & Cheng, S. K.-W. (2014) · Behaviour Research and Therapy · doi:10.1016/j.brat.2014.10.002 · Cited by 86
A protocol for a randomised active-controlled trial to evaluate the effects of an online mindfulness intervention on executive control, critical thinking and key thinking dispositions in a university student sample
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Theoretical Perspectives of Adherence to Web-Based Interventions: a Scoping Review
Ryan, C., Bergin, M., & Wells, J. S. (2018) · International Journal of Behavioral Medicine · doi:10.1007/s12529-017-9678-8 · Cited by 86
Understanding the usage of content in a mental health intervention for depression: an analysis of log data.
Van Gemert-Pijnen, J. E., Kelders, S. M., & Bohlmeijer, E. T. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.2991 · Cited by 84 · cited 1×
“It is thus very possible that dose-response relationships might become more apparent when the measures used to operationalize adherence match the goal of the technology [82,83].”
BackgroundSelf-citation
Effects of a Web-Based Computer-Tailored Game to Reduce Binge Drinking Among Dutch Adolescents: A Cluster Randomized Controlled Trial
Jander, A., Crutzen, R., Mercken, L. A. G., Candel, M. J. J. M., & de Vries, H. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.4708 · Cited by 75
Do adherence variables predict outcome in an online program for the prevention of eating disorders?
Manwaring, J. L., Bryson, S. W., Goldschmidt, A. B., Winzelberg, A. J., Luce, K. H., Cunning, D., Wilfley, D. E., & Taylor, C. B. (2008) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.76.2.341 · Cited by 73
Web-Based Video-Coaching to Assist an Automated Computer-Tailored Physical Activity Intervention for Inactive Adults: A Randomized Controlled Trial
Alley, S., Jennings, C. A., Plotnikoff, R. C., & Vandelanotte, C. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5664 · Cited by 69
Rates and Determinants of Uptake and Use of an Internet Physical Activity and Weight Management Program in Office and Manufacturing Work Sites in England: Cohort Study
Ware, L. J., Hurling, R., Bataveljic, O., Fairley, B. W., HURST, T. L., Murray, P., Rennie, K. L., Tomkins, C. E., Finn, A., Cobain, M. R., et al. (2008) · Journal of Medical Internet Research · doi:10.2196/jmir.1108 · Cited by 65
Do Web-Based Interventions Improve Well-Being in Type 2 Diabetes? A Systematic Review and Meta-Analysis
Hadjiconstantinou, M., Byrne, J. L., Bodicoat, D. H., Robertson, N., Eborall, H., Khunti, K., & Davies, M. J. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5991 · Cited by 64
Self-managed eHealth Disease Monitoring in Children and Adolescents with Inflammatory Bowel Disease
Carlsen, K., Jakobsen, C., Houen, G., Kallemose, T., Pærregaard, A., Riis, L. B., Munkholm, P., & Wewer, V. (2017) · Inflammatory Bowel Diseases · doi:10.1097/mib.0000000000001026 · Cited by 63
Who Follows eHealth Interventions as Recommended? A Study of Participants' Personal Characteristics From the Experimental Arm of a Randomized Controlled Trial
Reinwand, D. A., Schulz, D. N., Crutzen, R., Kremers, S. P., & de Vries, H. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.3932 · Cited by 62
A sorrow shared is a sorrow halved? A three-arm randomized controlled trial comparing internet-based clinician-guided individual versus group treatment for social anxiety disorder
Schulz, A., Stolz, T., Vincent, A., Krieger, T., Andersson, G., & Berger, T. (2016) · Behaviour Research and Therapy · doi:10.1016/j.brat.2016.07.001 · Cited by 62
Web-based depression treatment: Associations of clients׳ word use with adherence and outcome
van der Zanden, R. A., Curie, K., van Londen, M., Kramer, J., Steen, G. J., & Cuijpers, P. J. (2014) · Journal of Affective Disorders · doi:10.1016/j.jad.2014.01.005 · Cited by 61
The Effectiveness of Information Technology-Supported Shared Care for Patients With Chronic Disease: A Systematic Review
Kooij, L., Groen, W. G., & van Harten, W. H. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7405 · Cited by 59
Strategies for increasing adherence to an online smoking cessation intervention for college students
An, L. C., Perry, C. L., Lein, E., Klatt, C. M., Farley, D. M., Bliss, R., Hennrikus, D. J., Pallonen, U. E., Lando, H. A., Ahluwalia, J. S., et al. (2006) · Nicotine & Tobacco Research · doi:10.1080/14622200601039881 · Cited by 58
Usage of a Generic Web-Based Self-Management Intervention for Breast Cancer Survivors: Substudy Analysis of the BREATH Trial
van den Berg, S. W., Peters, E. J., Kraaijeveld, J. F., Gielissen, M. F., & Prins, J. B. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2566 · Cited by 53
Improving Adherence to Smoking Cessation Treatment: Intervention Effects in a Web-Based Randomized Trial
Graham, A. L., Papandonatos, G. D., Cha, S., Erar, B., Amato, M. S., Cobb, N. K., NIAURA, R. S., & Abrams, D. B. (2016) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntw282 · Cited by 52
Nonuse and Dropout Attrition for a Web-Based Mental Health Intervention Delivered in a Post-Disaster Context
Price, M., Gros, D. F., McCauley, J. L., Gros, K. S., & Ruggiero, K. J. (2012) · Psychiatry: Interpersonal and Biological Processes · doi:10.1521/psyc.2012.75.3.267 · Cited by 49
Predictors of adherence and outcome in internet-based cognitive behavior therapy delivered in a telephone counseling setting
Farrer, L. M., Griffiths, K. M., Christensen, H., Mackinnon, A. J., & Batterham, P. J. (2014) · Cognitive Therapy and Research · doi:10.1007/s10608-013-9589-1 · Cited by 47 · cited 1×
“In 23 studies, the intended usage was described as well (Category B operationalization) [51-73], and 5 studies reported the intended usage with a justification for this threshold (Category C operationalization) [74-78].”
Background
Uptake and adherence to an online intervention for cancer-related distress: older age is not a barrier to adherence but may be a barrier to uptake
Beatty, L., Kemp, E., Binnion, C., Turner, J., Milne, D., Butow, P., Lambert, S., Yates, P., Yip, D., & Koczwara, B. (2017) · Supportive Care in Cancer · doi:10.1007/s00520-017-3591-1 · Cited by 46
Web-Based Cognitive Training: Patient Adherence and Intensity of Treatment in an Outpatient Memory Clinic
Cruz, V. T., Pais, J. L., Alves, I., Ruano, L., Mateus, C., Barreto, R. D., Bento, V., Colunas, M., Rocha, N. P., & Coutinho, P. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3377 · Cited by 43
Feasibility, acceptability and efficacy of a web-based computer-tailored physical activity intervention for pregnant women - the Fit4Two randomised controlled trial
Hayman, M., Reaburn, P. R. J., Browne, M., Vandelanotte, C., Alley, S., & Short, C. E. (2017) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-017-1277-9 · Cited by 43
Exploring blended group interventions for depression: Randomised controlled feasibility study of a blended computer- and multimedia-supported psychoeducational group intervention for adults with depressive symptoms
Schuster, R., Leitner, I., Carlbring, P., & Laireiter, A.-R. (2017) · Internet Interventions · doi:10.1016/j.invent.2017.04.001 · Cited by 42
Improving adherence to web-based cessation programs: a randomized controlled trial study protocol
Graham, A. L., Cha, S., Papandonatos, G. D., Cobb, N. K., Mushro, A., Fang, Y., NIAURA, R. S., & Abrams, D. B. (2013) · Trials · doi:10.1186/1745-6215-14-48 · Cited by 39
Effects of the Web Behavior Change Program for Activity and Multimodal Pain Rehabilitation: Randomized Controlled Trial
Nordin, C. A., Michaelson, P., Gard, G., & Eriksson, M. K. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5634 · Cited by 38
Individual and Household Predictors of Adolescents’ Adherence to a Web-Based Intervention
Mâsse, L. C., Watts, A. W., Barr, S. I., Tu, A. W., Panagiotopoulos, C., Geller, J., & Chanoine, J. (2014) · Annals of Behavioral Medicine · doi:10.1007/s12160-014-9658-z · Cited by 34
Program Use and Outcome Change in a Web-Based Trauma Intervention: Individual and Social Factors
Wang, Z., Wang, J., & Maercker, A. R. A. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5839 · Cited by 32
A comparison of MOVE! versus TeleMOVE programs for weight loss in Veterans with obesity
Rutledge, T. R., Skoyen, J. A., Wiese, J. A., Ober, K. M., & Woods, G. N. (2016) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2016.11.005 · Cited by 32
Does parental and adolescent participation in an e-health lifestyle modification intervention improve weight outcomes?
Tu, A. W., Watts, A. W., Chanoine, J., Panagiotopoulos, C., Geller, J., Brant, R., Barr, S. I., & Mâsse, L. C. (2017) · BMC Public Health · doi:10.1186/s12889-017-4220-0 · Cited by 30
Access, Use and Completion of a Brief Disaster Mental Health Intervention among Hispanics, African-Americans and Whites Affected by Hurricane Ike
Price, M., Davidson, T. M., Andrews, J. O., & Ruggiero, K. J. (2013) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x13476230 · Cited by 28
Is online treatment adherence affected by presentation and therapist support? A randomized controlled trial
Alfonsson, S., Olsson, E. M. G., Linderman, S., Winnerhed, S., & Hursti, T. J. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.01.035 · Cited by 27
Feasibility, acceptability, and characteristics associated with adherence and completion of a culturally relevant internet-enhanced physical activity pilot intervention for overweight and obese young adult African American women enrolled in college
Joseph, R. P., Dutton, G. R., Cherrington, A., Fontaine, K. R., Baskin, M. L., Casazza, K., Lorch, D., Allison, J. J., & Durant, N. H. (2015) · BMC Research Notes · doi:10.1186/s13104-015-1159-z · Cited by 26
Optimising text messaging to improve adherence to web-based smoking cessation treatment: a randomised control trial protocol
Graham, A. L., Jacobs, M. A., Cohn, A. M., Cha, S., Abroms, L. C., Papandonatos, G. D., & Whittaker, R. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-010687 · Cited by 26
Web-based stress management for newly diagnosed cancer patients (STREAM-1): a randomized, wait-list controlled intervention study
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Feasibility of Implementing a Patient-Centered Postoperative Wound Monitoring Program Using Smartphone Images: A Pilot Protocol
Fernandes‐Taylor, S., Gunter, R. L., Bennett, K. M., Awoyinka, I., Rahman, S., Greenberg, C. C., & Kent, K. C. (2017) · JMIR Research Protocols · doi:10.2196/resprot.6819 · Cited by 26
Usage and Effectiveness of a Fully Automated, Open-Access, Spanish Web-Based Smoking Cessation Program: Randomized Controlled Trial
Mañanes, G., & Vallejo, M. Á. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3091 · Cited by 23
Attrition From an Internet-Based Psychological Intervention for Erectile Dysfunction: Who is Likely to Drop Out?
McCabe, M. P., & Price, E. (2009) · Journal of Sex & Marital Therapy · doi:10.1080/00926230903065963 · Cited by 20
Participant adherence to the Internet-based prevention program StudentBodies™ for eating disorders — A review
Beintner, I., Jacobi, C., & Taylor, C. B. (2014) · Internet Interventions · doi:10.1016/j.invent.2014.03.001 · Cited by 19
Impact of Seasonality on Recruitment, Retention, Adherence, and Outcomes in a Web-Based Smoking Cessation Intervention: Randomized Controlled Trial
Graham, A. L., Cha, S., Cobb, N. K., Ye, F., NIAURA, R. S., & Mushro, A. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2880 · Cited by 18
Exploring healthy eating among ethnic minority students using mobile technology: Feasibility and adherence
Rodgers, R. F., Franko, D. L., Shiyko, M. P., Intille, S. S., Wilson, K., O’Carroll, D., Lovering, M. E., Matsumoto, A., Iannuccilli, A., Luk, S., et al. (2015) · Health Informatics Journal · doi:10.1177/1460458214565950 · Cited by 15
Feasibility of an online intervention (STAK-D) to promote physical activity in children with type 1 diabetes: protocol for a randomised controlled trial
Blake, H., Quirk, H., Leighton, P. A., Randell, T. L., Greening, J. E., Guo, B., & Glazebrook, C. (2016) · Trials · doi:10.1186/s13063-016-1719-0 · Cited by 15
Increasing engagement with, and effectiveness of, an online CBT-based stress management intervention for employees through the use of an online facilitated bulletin board: study protocol for a pilot randomised controlled trial
Carolan, S., Harris, P. R., Greenwood, K., & Cavanagh, K. (2016) · Trials · doi:10.1186/s13063-016-1733-2 · Cited by 9 · cited 3×
“In 23 studies, the intended usage was described as well (Category B operationalization) [51-73], and 5 studies reported the intended usage with a justification for this threshold (Category C operationalization) [74-78].”
Background
Mobile Technology Improves Therapy-Adherence Rates in Elderly Patients Undergoing Rehabilitation—A Crossover Design Study
Mertens, A., Becker, S., Theis, S., Rasche, P., Wille, M., Bröhl, C., Finken, L., & Schlick, C. (2017) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-319-41652-6_28 · Cited by 5

Flagged first, then by citations

1 of the 84 references couldn’t be matched to a known paper.

Research works printed but not linked: 4

SmithWPlodererBWadleyGWebberSBorlandRTrajectories of Engagement and Disengagement with a Story-Based Smoking Cessation App20172017 CHI Conference on Human Factors in Computing SystemsMay 6-11, 2017Denver, ColoradoACM304556
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Sorted by citations

Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review
Jakob, R., Harperink, S., Rudolf, A. M., Fleisch, E., Haug, S., Mair, J. L., Salamanca-Sanabria, A., & Kowatsch, T. (2022) · Journal of Medical Internet Research · doi:10.2196/35371 · Cited by 446
Measuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies
Short, C. E., DeSmet, A., Woods, C., Williams, S. L., Maher, C., Middelweerd, A., Müller, A. M., Wark, P. A., Vandelanotte, C., Poppe, L., et al. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9397 · Cited by 400
Digital interventions for the treatment of depression: a meta-analytic review
Moshe, I., Terhorst, Y., Philippi, P., Domhardt, M., Cuijpers, P., Cristea, I., Pulkki-Råback, L., Baumeister, H., & Sander, L. B. (2021) · Psychological Bulletin · doi:10.1037/bul0000334 · Cited by 390
Barriers to and Facilitators of Engagement With Remote Measurement Technology for Managing Health: Systematic Review and Content Analysis of Findings
Simblett, S., Greer, B., Matcham, F., Curtis, H., Polhemus, A., Ferrão, J., Gamble, P., & Wykes, T. (2018) · Journal of Medical Internet Research · doi:10.2196/10480 · Cited by 270
A smartphone-based health care chatbot to promote self-management of chronic pain (SELMA): pilot randomized controlled trial
Hauser-Ulrich, S., Künzli, H., Meier-Peterhans, D., & Kowatsch, T. (2020) · JMIR mHealth and uHealth · doi:10.2196/15806 · Cited by 210

See all 331

Authors: Sieverink, F., Kelders, S. M., & van Gemert-Pijnen, J. E.
Affiliations: University of Twente (NL)
North-West University (ZA)
Corresponding author: Sieverink, Floor
ORCID: Sieverink, Floor
Kelders, Saskia M
van Gemert-Pijnen, Julia EWC
Venue: Journal of Medical Internet Research
Publisher: JMIR Publications Inc.
Published: 2017-12-06
Volume: 19
Issue: 12
Pages: e402
ISSN: 1438-8871 (ISSN-L)
DOI: 10.2196/jmir.8578
PMC: PMC5738543
OpenAlex: W2774320409
Semantic Scholar: a1db2bb515ffdc128b4290c1c2d3f04a5b280310
Cited by: 331
References: 84
Citations by year: 2026: 28 · 2025: 33 · 2024: 35 · 2023: 38 · 2022: 45 · 2021: 51 · 2020: 48 · 2019: 31 · 2018: 21 · 2017: 1
FWCI: 4.86
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: Journal of Medical Internet Research (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
University of Twente Research Information (publishedVersion, cc-by)
University of Twente Research Information (submittedVersion, cc-by)
Topics: Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
Keywords: eHealth technologies, technology adherence, intended use, mental health technology, eHealth evaluation, operating mechanism
MeSH terms: Humans, Patient Compliance, Telemedicine, Biomedical Technology

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Clarifying the Concept of Adherence to eHealth Technology: Systematic Review on When Usage Becomes Adherence

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