A PHR typically comprises of demographic info, physical exam and medical history, clinical and hospital discharge notes, diagnostics test results and medications.
From the lit review we identified some PHR attributes that are related to medication adherence. PHRs have other attributes for example bill viewing and payment, drug to drug interaction systems, immunization records etc. We do not argue that these attributes do not help with medication adherence, we are simply stating the fact that we did not find evidence in the literature that they do so. The included attributes as well as others that we might omit i.e. bill viewing, assist patients with the general purpose of PHR which is to decrease care cost and make the management of diseases easier.
Link to book
Construct validity refers to the degree to which inferences can legitimately be made from the operationalizations in your study to the theoretical constructs on which those operationalizations were based. We might think of construct validity as a “labeling” issue. When you implement a program that you call a “Head Start” program, is your label an accurate one? When you measure what you term “self esteem” is that what you were really measuring?
We said yesterday that medication adherence is a definite context; hence we do not need to make sure that what we mean by it everyone else means the same. Obviously since PHR is something we can see/touch/point at there is no issue of construct validity.
Do we need to check the questionnaire as to have construct validity? (makes no sense to me to do so, since both our variables are well defined?!)
In face validity, you look at the operationalization and see whether “on its face” it seems like a good translation of the construct. This is probably the weakest way to try to demonstrate construct validity. For instance, you might look at a measure of math ability, read through the questions, and decide that yep, it seems like this is a good measure of math ability (i.e., the label “math ability” seems appropriate for this measure). Of course, if this is all you do to assess face validity, it would clearly be weak evidence because it is essentially a subjective judgment call. We can improve the quality of face validity assessment considerably by making it more systematic. For instance, if you are trying to assess the face validity of a math ability measure, it would be more convincing if you sent the test to a carefully selected sample of experts on math ability testing and they all reported back with the judgment that your measure appears to be a good measure of math ability.
Content validity refers to how accurately an assessment or measurement tool taps into the various aspects of the specific construct in question. In other words, do the questions really assess the construct in question, or are the responses by the person answering the questions influenced by other factors? This approach assumes that you have a good detailed description of the content domain, something that’s not always true.
In predictive validity, we assess the operationalization’s ability to predict something it should theoretically be able to predict. For instance, we might theorize that a measure of math ability should be able to predict how well a person will do in an engineering-based profession. We could give our measure to experienced engineers and see if there is a high correlation between scores on the measure and their salaries as engineers. A high correlation would provide evidence for predictive validity — it would show that our measure can correctly predict something that we theoretically think it should be able to predict.
In concurrent validity, we assess the operationalization’s ability to distinguish between groups that it should theoretically be able to distinguish between. For example, if we come up with a way of assessing manic-depression, our measure should be able to distinguish between people who are diagnosed manic-depression and those diagnosed paranoid schizophrenic. If we want to assess the concurrent validity of a new measure of empowerment, we might give the measure to both migrant farm workers and to the farm owners, theorizing that our measure should show that the farm owners are higher in empowerment. As in any discriminating test, the results are more powerful if you are able to show that you can discriminate between two groups that are very similar.
In convergent validity, we examine the degree to which the operationalization is similar to (converges on) other operationalizations that it theoretically should be similar to. For instance, to show the convergent validity of a Head Start program, we might gather evidence that shows that the program is similar to other Head Start programs. Or, to show the convergent validity of a test of arithmetic skills, we might correlate the scores on our test with scores on other tests that purport to measure basic math ability, where high correlations would be evidence of convergent validity.
In discriminant validity, we examine the degree to which the operationalization is not similar to (diverges from) other operationalizations that it theoretically should be not be similar to. For instance, to show the discriminant validity of a Head Start program, we might gather evidence that shows that the program is notsimilar to other early childhood programs that don’t label themselves as Head Start programs. Or, to show the discriminant validity of a test of arithmetic skills, we might correlate the scores on our test with scores on tests that of verbal ability, where low correlations would be evidence of discriminant validity.
True Score Theory – random error
The variability of your measure is the sum of the variability due to true score and the variability due to random error, which sometimes considered noise.
It reminds us that most measurement has an error component. Second, true score theory is the foundation of reliability theory. A measure that has no random error (i.e., is all true score) is perfectly reliable; a measure that has no true score (i.e., is all random error) has zero reliability. Third, true score theory can be used in computer simulations as the basis for generating “observed” scores with certain known properties.
Measurement Error – systematic error
systematic error is sometimes considered to be bias in measurement.
Reducing Measurement Error
- Pilot test your instruments, getting feedback from your respondents regarding how easy or hard the measure was and information about how the testing environment affected their performance.
- If you are gathering measures using people to collect the data (as interviewers or observers) you should make sure you train them thoroughly so that they aren’t inadvertently introducing error.
- When you collect the data for your study you should double-check the data thoroughly. All data entry for computer analysis should be “double-punched” and verified. This means that you enter the data twice, the second time having your data entry machine check that you are typing the exact same data you did the first time.
- You can use statistical procedures to adjust for measurement error. These range from rather simple formulas you can apply directly to your data to very complex modeling procedures for modeling the error and its effects.
- One of the best things you can do to deal with measurement errors, especially systematic errors, is to use multiple measures of the same construct. Especially if the different measures don’t share the same systematic errors, you will be able to triangulate across the multiple measures and get a more accurate sense of what’s going on.
Theory of Reliability
In research, the term reliability means “repeatability” or “consistency”. A measure is considered reliable if it would give us the same result over and over again (assuming that what we are measuring isn’t changing!).
If our measure, X, is reliable, we should find that if we measure or observe it twice on the same persons that the scores are pretty much the same.
the error score is assumed to be random.
Reliability is a ratio or fraction. we can’t compute reliability because we can’t calculate the variance of the true scores
we can estimate the reliability as the correlation between two observations of the same measure.
Types of Reliability
- Inter-Rater or Inter-Observer Reliability
Used to assess the degree to which different raters/observers give consistent estimates of the same phenomenon.
- Test-Retest Reliability
Used to assess the consistency of a measure from one time to another.
- Parallel-Forms Reliability
Used to assess the consistency of the results of two tests constructed in the same way from the same content domain.
- Internal Consistency Reliability
Used to assess the consistency of results across items within a test.
from Robson, C., & McCartan, K. (2016). Real world research : a resource for users of social research methods in applied settings. Chichester, West Sussex, United Kingdom : Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=cat01619a&AN=up.1189250&site=eds-live
- The positivist view of science is discredited.
- You can’t leave your humanity behind when doing research.
- The quantitative and qualitative research have traditionally been considered as different research paradigms in the sense of distinctive belief systems carrying with them clear philosophical assumptions.
- Quantitative: a focus on behavior feature (i.e. what people do or say)
- Qualitative: a focus on meanings feature
We developed a data extraction sheet (based on the data extraction forms for qualitative studies National Institute for Health and Care Excellence (NICE)  and the data extraction chapter from the Cochrane Collaboration ), pilot-tested it on ten randomly-selected included studies, and refined it accordingly. One review author extracted the following data from included studies and the second author checked the extracted data. Disagreements were resolved by discussion between the two review authors; if no agreement could be reached, it was planned a third author would decide. We contacted two authors for further information, one of them responded and clarified the type of data that were presented in the paper.
1 Nice SCI for E. Data extraction forms for qualitative studies. In: A NICE-SCIE Guideline on Supporting People With Dementia and Their Carers in Health and Social Care. The British Psychological Society and Gaskell 2006. 66–8. doi:978-1-85433-451-0
2 Noyes J, Hannes K, Booth A, et al. Qualitative research and Cochrane reviews. In: Higgins JPT, Green S, eds. Cochrane Handbook for Systematic Reviews of Interventions Version 5.3.0 (updated October 2015). The Cochrane Collaboration 2015. http://qim.cochrane.org/supplemental-handbook-guidance[Top]
Again these are my personal thoughts and opinions.
- In case of doubt I include the study
- There is a lot of IT research in the field, but developers design a system and they do not seem to test it in practice. They just claim that improves medication adherence.
- HIV/AIDS is now often characterized as a chronic yet manageable disease.
- The publications do not typically mention the term PHR but they state something similar to “mobile application linked to the GP” and any imaginative variation of this sentence; thus I think a very clear PHR definition is needed before the actual write up starts. Also mHealth is a term much more used than PHR.
- Publications also use the term medication reconciliation aka the best possible medication history. These publications tend to link this term with PHR but not with medication adherence.
- Would it be unadvised to assume that war veterans suffer from chronic conditions, without the authors explicitly state so?
- Studies that used two modes of intervention are included if the primary mode is PHR-based.
- A few publications use the term medication misuse, instead of medication adherence. I think it is safe to assume that if a patient does not misuse their medication they adhere to it.
- Good adherence encompasses multiple dimensions, including intensity and timing of use according to prescription (compliance), continuous use (persistence) and correct use (technique). van Boven, J. F., Trappenburg, J. C., van der Molen, T., & Chavannes, N. H. (2015). Towards tailored and targeted adherence assessment to optimise asthma management. Npj Primary Care Respiratory Medicine, 25(1), 15046. https://doi.org/10.1038/npjpcrm.2015.46
- What impact does PHR intervention have in medication adherence? (positive, negative, none)
- What impact does PHR intervention have in patient’s attitude towards starting a new medication?
The screening process finished and resulted in the inclusion of 130 articles.
The following statements are my personal opinions, thoughts and generalizations of what I have read in titles/abstracts/keywords so far.
- Medication adherence and literacy is a hot topic
- Medication adherence, literacy and technology is something that I have not encountered yet
- A lot of research in mental health conditions
- Way too few RTCs so far I identified 2 out of 200 for further reading
- There is clear link on medication adherence and patient behavior changes
- A lot of studies say “technological interventions” without specifying
- I decided to include the studies that I am not too sure about
- Way too few studies deal with comorbidities/polypharmacy ( found 3 so far)
- A lot of studies separate people like black/whites or Latino and poor or rich and old/young/kids
- Clear link between technology and self-management of a disease
- Medication adherence and Asthma has a lot of papers
- Instead of saying medication adherence, papers say behavior change in the way patients live or take their medication. Is it because they do not explore just adherence or is it because they do not like the term and the implications of it?
- There is a lot of research in similar fields with us in China.
- There are a lot of PHR vendors in USA
- Vast majority of the researches have 6 months to a year length
- Again the researches do not focus on just medication adherence, but also patient’s other life aspects
- At the papers that have abstracts from the xyz conference, meeting, whatever, I searched for the keywords “medication adherence”, PHR and “personal health record” in order to save time and not to read nice but utterly irrelevant abstracts