Tag Archives: self-efficacy

Understanding Self-Efficacy and the Design of Personal Informatics Tools

Adrienne Andrew is a PhD candidate in Computer Science at the University of Washington. She is interested in studying how people use food diaries on mobile phones: what challenges “typically motivated” users have, balance between capturing less detailed yet still valuable food information, and identifying new ways to organize food databases to support a wider range of dietary analysis.


One primary concern for the field of personal informatics (PI) is supporting people in making changes in their life. A driving theory for pesonal informatics (PI) designers and researchers is Social Cognitive Theory [Bandura, 1977], which posits that a person’s behavior, environment and inner qualities all contribute to how a person functions. This theory has been applied to understanding how people learn, how social environments impact what people do, and how people regulate their own behavior. A key component in this theory is self-efficacy (SE), which is summarized as a belief in one’s abilities.

The question I pose to both PI researcher and self-quantifiers is if your experiences support whether self-efficacy truly reflects intent and ability to engage in key behavior change strategies.

Measuring Self-Efficacy

SE is traditionally measured by self-report. To develop SE measurements for a particular domain, researchers use open-ended approaches to identify common challenges and barriers to the problem. They then develop a series of statements of the form “How confident are you that you can [achieve goal] even though [challenge]?” with a 4-unit response scale ranging from “Cannot do it” to “Highly certain can do”. An example of a statement is “How confident are you that you can stick to a healthy eating plan after a long, tiring day at work?” SE measures provides valuable feedback about whether an intervention is supporting adherence to behavior change strategies, and indicate whether participants complete the study with an intention to continue.
This is an important feature for PI researchers: we are familiar with a domain and common challenges, so can build the scales easily; we usually use short-term studies to indicate long-term impact; and properly designed scales can help us to discover where a PI tool breaks down.

Self-Efficacy Influencers

Now that we have described how SE can be measured and its relevance to PI researchers, it is important to acknowledge factors that may impact the measurements as applicable to PI tools. In addition to basic usability (which I would also argue is more important to the “common consumer” as opposed to highly-motivated quantified-selfers), a user’s goals (internal motivation) and trust in the technology are key.

How well the tool matches the user’s goals.

This is a point that is likely more important to researchers than to quantified-selfers. It refers to both a goal the user has and that the user has a belief in what they need to do in order to attain that goal. A user who is trying to lose weight may choose to focus on restricting caloric intake as well as increasing caloric expenditure, or choose to focus on only one of those areas. Social cognitive theory says these beliefs are based on what the user has observed amongst their peers, and how similar or different the user is from their peers.

We observed this in the BALANCE studies. BALANCE consisted of a food diary to capture caloric intake, an automatic physical activity detection platform to measure caloric expenditure, and a visualization that provided real-time feedback of the person’s caloric intake/expenditure balance throughout the day, all on a mobile phone. Overall, about 40 people participated in the evaluation by carrying the phone and tracked their food intake for 3 days.

One recurring theme in the feedback was that tracking food intake with such detail was too much work, and would only be worth it if they had a medical condition that made it very important to keep detailed records. However, some participants wanted to reflect on a coarser grained summary of their dietary intake for general health and disease prevention. There participants had a different wellness goal, and therefore didn’t have the internal motivation to make this tool useful to them.

Understanding the underlying technology.

Another factor is how well the user understands the technology, or more specifically, how the technology may fail. Part of the BALANCE project was using sensors to identify and calculate calories expended via activity throughout an entire day. Other related tools are GPS-based run trackers that use GPS to track the location, duration and other metrics of the run. Technologies that use sensors to identify bouts of physical activity have some level of uncertainty associated with the recognition. This uncertainty comes from a variety of sources, such as parameters that reflect a tradeoff between power consumption and accuracy. GPS trace quality depends on terrain and location of satellites in the sky.

A recent New York Times article reflects the concern of GPS run tracker users. Runners sometimes measure certified race courses, and report discrepancies to the organizers. These runners appear to trust the technology more than the organization. In the case of BALANCE (which exposes less detailed data about the calorie calculation and depends on more parameters), some users reported a feeling that the calculation “didn’t feel right”, but were unable to express how they thought it might be wrong. With both of these examples, the uncertainty with the technology could impact measures of SE. This raises the question of what other factors influence a person’s trust in the technology, as well as how SE may be impacted, and how it may vary from person to person.

So, I pose this question to quantified selfers: What do you track, and what aspects of the tools you use impact whether or not you can or will keep using them?

 

This article is a summary of a position paper by Chloe Fan and her colleagues that will be discussed at the Personal Informatics in Practice workshop at CHI 2012 in Austin, TX on May 6, 2012. The workshop will be a gathering of researchers, designers, and practitioners exploring how to better support personal informatics in people’s everyday lives.

 

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What is Self-Efficacy?

LittleEngineThatCouldn't.pngMy curiosity about real world applications of objective techniques of self-discovery and self-management let me recently to some classic work by [Albert Bandura](http://en.wikipedia.org/wiki/Albert_Bandura), who introduced the idea of [self-efficacy](http://www.des.emory.edu/mfp/self-efficacy.html) into cognitive psychology. Self-efficacy is different than self-confidence or self-esteem. It is not a personality trait, or a set of general beliefs about oneself. Rather, it is a subjective expectation of how likely you are to succeed at some specified goal. You can have high self-efficacy in one area, and low self-efficacy in another. Although these expectations are subjective, they can be measured objectively by researchers, who ask a standard set of multiple choice questions. The design of these questionnaires is itself a [research area](http://userpage.fu-berlin.de/%7Ehealth/engscal.htm) of some interest, but the important thing is that, in the three decades since Bandura first introduced the concept, he and others have proven that it can be measured, that it can be influenced, and, most importantly, correlates with the actual probability of success in tasks that require motivation and persistence.
In outline, this seems obvious. If you don’t believe you can do something – quit smoking; learn a foreign language; overcome a phobia; etc. – you are less likely to persist in the face of discouragement. Any wise counselor could tell you to pace yourself, and tackle challenges in an ascending order of difficulty, so as not to burn out too soon or fall into despair.
What’s interesting about Bandura’s work, and those who followed him, was not the confirmation of penny wisdom, but the development of objective measures of self-efficacy that allowed experimentation. At what stage of behavioral change is self-efficacy most important? How can it be effectively influenced? Research on self-efficacy has influenced treatment for addiction, chronic pain, stress, depression, and obesity; it’s also played a role in athletic training and physical rehabilitation.
For the aspiring self-quantifier, the history of self-efficacy research is full of promising hints. Once the notion of self-efficacy has been separated from the more general concept of self-esteem, it’s easier to notice specific areas where low self-efficacy may interfere with learning or achievement. Low self-efficacy leads to avoidance behavior. We don’t try things we believe we can’t do. In Bandura’s original paper, he points to research showing that this avoidance behavior can persist even when there is no conscious anxiety, no negative emotional arousal. We simply skirt the issue. Perhaps we even convince ourselves that it is not necessary, or a waste of time. Engrained habits of avoidance can become nearly invisible to our conscious reflection, due to how effectively they guard us from the bad consequences we believe will result from failure.
The research results influenced by Bandura’s paper are applied mainly by teachers, coaches, and health care providers to boost self-efficacy in training situations. But it can also be a tool of self-investigation. We can hunt for deficits in self-efficacy that create unnecessarily limits using the following question:
“If I were good at it, I would definitely want to (do/learn) [X]”
It may well turn out that some of our deficits are not due to a lack of inherent capacity, but to a failure of persistence and motivation stemming from a lack of self-efficacy. Improve self-efficacy, and previously inaccessible achievements come within reach. This is an especially promising approach when the goal is simply average proficiency. Obviously, increases in self-efficacy cannot affect inherent limits. But in areas that one has avoided do to fear of failure, average can be a truly excellent result.
Noticing and correcting low-self efficacy can be difficult. The power of the concept is linked to its specificity. Self-efficacy is efficacy for something. It has an explicit goal. Complex tasks consists of lots of different parts, and of course they are situated within the context of our whole life. So self-efficacy can get lost in the noise of other phenomenon – general self-esteem, physical tiredness, environmental stresses and distractors. That’s why instruments for measuring and tracking self-efficacy are crucial for regulating it. In a future post, I’ll do a quick pass on some of the technical possibilities for tracking self-efficacy. But for now, here’s the original Bandura paper (PDF), which boasts more than 5500 citations, according to [Google Scholar](http://scholar.google.com/scholar?q=Self+Efficacy&hl=en&lr=&btnG=Search).
Excerpts:
> An outcome expectancy is defined as a person’s estimate that a given behavior will lead to certain outcomes. An efficacy expectation is the conviction that one can successfully execute the behavior required to produce the outcomes. Outcome and efficacy expectations are differentiated, because individuals can believe that a particular course of action will produce certain outcomes, but if they entertain serious doubts about whether they can perform the necessary activities such information does not influence their behavior.
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> The principal assumption that defensive behavior is controlled by anxiety arousal is also disputed by several lines of evidence. Autonomic arousal, which constitutes the principal index of anxiety,-is not necessary for defensive learning. Because autonomic reactions take much longer to activate than do avoidance responses, the latter cannot be caused by the former. Studies in which autonomic and avoidance responses are measured concurrently indicate that these two modes of activity may be partially correlated in the acquisition phase but are not causally related (Black, 1965). Avoidance behavior, for example, can persist long after autonomic reactions to threats have been extinguished. Surgical removal of autonomic feedback capability in animals has little effect on the acquisition of avoidance responses (Rescorla & Solomon, 1967). Maintenance of avoidance behavior is even less dependent on autonomic feedback. Once defensive behavior has been learned, depriving animals of autonomic feed back does not hasten the rate at which such activities are extinguished.
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> The theory presented here posits a central processor of efficacy information. That is, people process, weigh, and integrate diverse sources of information concerning their capability, and they regulate their choice behavior and effort expenditure accordingly.
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> It will be recalled that efficacy expectations are presumed to influence level of performance by enhancing intensity and persistence of effort.

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